ROGUE: Red-team every way a high-stakes AI agent can fail — then generate the fix

Find it. Prove it. Fix it. The red-and-blue team that never sleeps.

_{Independent, reproducible evidence of how an AI agent fails — and the verified fix to close it — before you ship. Open-source, runs in 2 minutes, no vendor lock-in.}

ROGUE red-teams your AI agent against live open-web jailbreaks, grades each with a human-calibrated judge, and hands you signed, reproducible evidence — plus a suggested fix for what breaks — before you deploy. Backed by 11,973 calibrated-judge trials across 8 production models, with a counterintuitive measured finding: most claimed jailbreaks don't survive a real deployment — reproduction collapses 40% → 4%.

ROGUE measures every place a high-stakes AI agent can go wrong: whether the model can be broken by a live jailbreak or prompt-injection, whether the tools it can invoke can be turned against it (tool-use hijack, indirect injection via tool outputs), whether the human oversight around it is meaningful, and whether the memory it accumulates stays contained. Each is scored against an independent, continuously-refreshed standard and emitted as a reproducible signed record — and it closes the loop: every scan generates a fix for each breach, and on demand ROGUE re-tests that fix to prove it holds without over-blocking (you own the runtime, so ROGUE never sits in your request path).

▶ See a real breach in 20 seconds — no key, no signup

pip install rogue-live-redteam && rogue try

A live ATTACKER → MODEL → JUDGE red-team in your terminal, then ROGUE's real measured breach rates across 8 production models. Then point it at your own deployment — rogue scan --endpoint <your-api> --system-prompt <yours> — for a scored report of exactly which attacks break it: the exact attack, your model's response, and a remediation hook for each finding.

→ Full 5-minute walkthrough, clean machine to report.html: QUICKSTART.md

📄 Research — ROGUE's findings are four papers, each reproducible from this repo (frozen data + a script per result): open-web jailbreaks mostly don't reproduce in deployment (40% → 4%); a per-type judge gate reaching 89.3% human agreement (3rd of 5, tied with the frontier classifiers); evaluation allocation as a capability lever (8/20 vs 0/20 candidates graduate, Fisher p = 0.003); and canary leakage from shared agent memory that tracks alignment, not model size (85% on a weak model). → PAPERS.md

🔒 Security & Trust — ROGUE generates and verifies fixes but never sits in your request path — you own the runtime. Scans run locally against your own endpoint; your prompts, system prompts, and keys never leave your machine. Released data is derived-only, never raw scraped corpora (RESPONSIBLE_RELEASE.md). Found a security issue? SECURITY.md.

See it live

• Dashboard: https://rogue-eosin.vercel.app (live, deployed).
• Trailer: watch the 45-second trailer on YouTube (preview below).
• Dataset: 298 harvested attack primitives across 15 families (the open-web-harvested slice of a 459-primitive live corpus), MIT-licensed and access-gated (defensive-research-only terms; see RESPONSIBLE_RELEASE.md).
• In Slack: point a Slack incoming webhook at ROGUE and the daily threat brief plus every new HIGH/CRITICAL breach post straight to your workspace (the platform integration also files findings to Jira). ROGUE comes to where your team already triages.

ROGUE_1080p_av1.mp4

Why ROGUE

Other LLM red-teams run a fixed attack set you have to keep updating. ROGUE is the only one that does all of this together:

• Harvests on a schedule. New jailbreaks and prompt-injections pulled from 15 open-web sources on a recurring cron — scraping is free and keyless (scraper-agnostic, no scraper is a dependency; extraction runs on any LLM you choose, incl. a local one), so the threat DB keeps refreshing without manual runs. (The breach-rate measurements are periodic measured snapshots, re-run deliberately, not a continuously-updating number.)
• Reproduces against your exact config. Your model, its system-prompt, and its tools — not a generic safety benchmark. ROGUE gives the model a real function-calling surface and runs a multi-turn agent loop, so it catches tool-use hijack and indirect injection, not just text jailbreaks: safe simulated tools by default, or point it at your own MCP tool server for real execution (dangerous tools stay recorded-not-executed).
• Is queryable over MCP, both ways. It harvests through MCP and serves results through its own MCP server, so you can ask "what breaches a model like mine?" from inside Cursor or Claude. No other red-team closes that loop.
• Measures three surfaces, signed. The model surface is deep and paper-backed; the human approval gate and the shared skill-pool are two further instruments at proof-of-concept scale. Each is scored against an independent answer key and emitted as a tamper-evident attestation.
• Runs on the LLM you choose. The judge and extraction models are configurable (JUDGE_MODEL): any provider or a local model (Ollama via OPENAI_BASE_URL), not locked to one vendor. Targets reach open-weight models too — a Featherless route puts Qwen / DeepSeek / GLM / Mistral in the same panel as OpenAI-native endpoints, so cross-model comparisons aren't single-vendor.
• ★ Budget-aware by design — scans are measurably cheaper, live in prod. ROGUE treats the trial budget as a decision problem, not a fixed count: sequential early-stopping (SPRT) resolves a clearly-safe/broken attack in ~4–6 trials instead of 12 (measured live A/B: ~48% fewer model+judge calls, breach rate unchanged), and a confidence-gated cascade judge grades flat refusals with a free regex so the paid frontier judge is spent only on ambiguous cases (18–46% fewer paid judge calls, 99.8% agreement). Both are on in production today with no quality loss — the "Adaptive Budget-Aware Red-Team Evaluation" work; nobody else treats the trial budget as sequential. → Capabilities

Each ingredient exists somewhere; no competitor does the whole combination. That is what makes ROGUE a continuous, queryable, multi-surface red-team rather than a one-off scan.

Use it in 30 seconds

What needs a key — straight answer: the demo is genuinely keyless; scanning a real model needs that model's key, and grading/harvesting need an LLM you choose (any provider, or a local Ollama → ~$0). The open-web scraping is always free and keyless.

Action	What it needs
rogue try	Nothing — mock target + keyless heuristic judge, fully offline
rogue scan	your target model's API key (the heuristic judge stays keyless)
rogue scan --judge calibrated	target key + a judge LLM key
Harvest (clone the repo)	free, keyless scraping + an LLM extraction key (any provider, incl. local)

See your first breach in 20 seconds (no key, no signup)

pip install rogue-live-redteam
rogue try        # 20s, offline, zero keys: real breach rates + a shareable card
rogue setup      # install the best free scraper (crawl4ai) — prep for harvesting from a repo clone

rogue try runs a live ATTACKER → MODEL → JUDGE red-team in your terminal, fully offline and zero keys, then overlays ROGUE's real measured breach rates across 6 production text models + 2 audio targets (11,973 calibrated-judge trials; the two audio targets are sampled lighter, ~185 trials each) and drops a shareable breach card:

For attacks that scale (many-shot / long-context), a single pass isn't enough — ROGUE sweeps the dimension and reports a robustness threshold: the context length at which each model caves. The cross-model board is live on the leaderboard; the measured finding is counterintuitive — for an explicit harmful ask, length barely matters (most models break at the smallest 2K setting), and the spread is between models, not lengths: on a 128K many-shot ladder of AdvBench harmful asks, GPT-OSS-120B holds at every length while DeepSeek-V4-Pro caves 97% of the time.

Then scan your model. The target is your own deployment: any OpenAI-compatible --endpoint plus your real --system-prompt (that's what makes it a deployment red-team, not a bare-model test). Pass --provider/--model instead to hit a hosted model by name:

rogue scan --endpoint https://api.your-co.com/v1 --model your-model --system-prompt-file ./prompt.txt
rogue scan --provider openai --model gpt-5.4-nano --judge calibrated   # …or a hosted model by name

Every scan drops the same shareable breach card as rogue try (--no-card to skip), now with your model's real numbers.

• Judge: defaults to a keyless heuristic (no API key). --judge calibrated grades with the v3 LLM judge, and that one uses your judge key (e.g. ANTHROPIC_API_KEY / JUDGE_MODEL's provider).
• Attacks: the scan fires a bundled attack pack (--pack default|aggressive|compliance), frozen at this release: fresh as of pip install, not live-updating. The continuously-harvested live corpus drives the hosted dashboard plus the public corpus; to run that live open-web harvest locally, use rogue setup (above) and see Run the harvest free.

Compare any model on the public leaderboard, or browse the measured attack corpus (every attack tagged with which models it actually breaches, not an unverified prompt dump).

Query ROGUE from your IDE (hosted MCP, zero setup)

The MCP server is mounted into the live API, so there is nothing to clone or run:

https://rogue-private.onrender.com/mcp/

The dashboard home has one-click Add to Cursor / Add to VS Code buttons; for Claude Desktop, add it as a custom connector. It exposes ~19 tools: read-only corpus/breach queries plus scan / report / benchmark actions. Full tool list and local install: MCP integration below.

Get a scored report, locally, no account

The CLI and Python SDK run a full scan against your own target today and emit a scored report (report.to_html() / JSON from the SDK, plus a CISO-ready PDF via the report service) on the same engine as the dashboard, no signup, nothing to buy. A FastAPI /v1 server (POST /v1/scans + OpenAPI spec) is included in the self-hosted stack (below) for programmatic access.

Run it locally: the full app (dashboard + API)

Self-host the whole thing (Postgres + API + the Next.js dashboard) with one command. It migrates and seeds a redacted snapshot of the real all-time breach matrix on startup, so every surface is fully populated on first boot, no scan and no keys. (The attack payloads and model responses are redacted to [redacted], exactly like the public site; the verdicts/rates are the real ones.)

git clone https://github.com/nguiaSoren/ROGUE && cd ROGUE
cp .env.example .env                                       # demo data needs no keys
docker compose -f docker-compose.full.yml up -d            # detached: ~30s to migrate, seed, and start

Open http://localhost:3000: /feed, /matrix, /analytics, and /brief run against your own local instance, no account and no hosted site required. (Follow startup with docker compose -f docker-compose.full.yml logs -f.)

Fill it with your model's data. ROGUE scans a model endpoint (any OpenAI-compatible API URL, your gateway or a hosted provider), not local files. The stack runs detached, so stay in the same terminal: install the rogue CLI on the host and point it at your endpoint with --persist so each result is written into the same DB the dashboard reads:

pip install rogue-live-redteam                            # the CLI, on the host (or: pip install -e . from this clone)
export ANTHROPIC_API_KEY=sk-ant-...                       # the judge that grades each response (or repoint JUDGE_MODEL)
rogue scan --endpoint https://api.company.com/v1 --model my-model --persist --config-name "my-bot"
# (writes to $DATABASE_URL; its local default already matches the stack's Postgres, so no config needed)

Then open http://localhost:3000/matrix?config=my-bot: the breach matrix scoped to your deployment. (The judge LLM costs API spend per scan; point JUDGE_MODEL at a local model, Ollama via OPENAI_BASE_URL, to keep it ~$0.)

Want a dashboard that's only your data? Bring the stack up with SEED_DEMO=0 and the DB starts empty; then every surface (/feed, /matrix, /analytics, /brief) shows nothing but your own scans, no demo rows to filter past:

SEED_DEMO=0 docker compose -f docker-compose.full.yml up -d   # empty DB, detached
rogue scan --endpoint https://api.company.com/v1 --model my-model --persist --config-name my-bot
# → http://localhost:3000 (every surface is now 100% your data)

Just the backend API, no dashboard (for development)

Skip the frontend, bring up a plain Postgres and run the API with hot-reload:

git clone https://github.com/nguiaSoren/ROGUE && cd ROGUE
cp .env.example .env          # add your keys
docker compose up -d && uv sync --extra dev
uv run alembic upgrade head && uv run python scripts/ops/seed_demo_data.py
uv run uvicorn rogue.api.main:app --reload

Scan your own model: the SDK

Install from PyPI for the rogue CLI + Python SDK, no clone needed (Python 3.11+):

pip install rogue-live-redteam

Scan any OpenAI-compatible target in three lines (plus a judge key, since ROGUE grades every response; see docs/SDK.md):

from rogue import Client
client = Client(
    endpoint="https://api.company.com/v1", api_key="sk-...",   # or Client(provider="openai")
    system_prompt="<your production system prompt>",           # red-team your REAL deployment, not a bare model
)
report = client.scan(pack="aggressive", budget=10.0)
print(report.summary()); report.to_html("scan.html")

…or from the CLI: rogue scan --provider openai --pack aggressive --system-prompt-file ./system_prompt.txt (--system-prompt "…" for inline; both also work with --persist). Pick your scrape backend and judge model in docs/harvest-backends.md.

No API key handy? Clone the repo and run the offline demo (mocked target + judge → an HTML report): PYTHONPATH=src python3 examples/sdk_quickstart.py.

Integrations

ROGUE meets your team where it already works:

Surface	Status	What you get
Your IDE (MCP)	✅ Available now · keyless	One config block in Claude Desktop / Cursor / Windsurf / VS Code; the editor's agent queries the live threat DB on the spot, read-only corpus/breach queries plus scan / report / benchmark action tools. https://rogue-private.onrender.com/mcp
Your chat & tracker (Slack + Jira)	✅ Slack alerts now	Point a Slack incoming webhook (SLACK_WEBHOOK_URL) at ROGUE and the daily threat brief + new CRITICAL/HIGH breaches post to your workspace automatically. Jira findings file via the MCP action tools (send_slack_alert / create_jira_ticket). Setup
API & SDK (REST /v1 + Python)	✅ runs locally	The Python SDK runs real scans today against your own target (pip install rogue-live-redteam; from rogue import Client, see docs/SDK.md). A FastAPI /v1 server + OpenAPI spec ship in the self-hosted stack.
Your CI (GitHub Action)	✅ shift-left gate	Add one uses: block to a pull_request workflow; ROGUE red-teams your deployment on every PR and fails the merge on any HIGH/CRITICAL breach (overridable). Setup

Gate your CI

Red-team your model on every pull request and block the merge on a HIGH/CRITICAL breach. Drop this into .github/workflows/rogue-scan.yml:

- uses: nguiaSoren/ROGUE@v1
  with:
    endpoint: https://gateway.your-company.com/v1
    model: your-deployed-model
    system-prompt-file: prompts/production-system-prompt.txt
    fail-on: high
    api-key: ${{ secrets.ROGUE_TARGET_KEY }}

Inputs, fail policy, and the security note are in docs/ci-action.md; a full copy-paste workflow is at examples/github-action/rogue-scan.yml.

What ROGUE does

Five-layer pipeline: Harvest → Extract → Dedupe → Reproduce → Diff.

1. Harvest. 15 open-web sources via a fully scraper-agnostic fetcher (scraping is free/keyless, bring any scraper — none required; the extraction step calls an LLM you choose).
2. Extract. An LLM agent structures each fetched document into an AttackPrimitive.
3. Dedupe. pgvector cosine similarity clusters near-duplicate attacks, with surface-obfuscation canonicalization (leetspeak/homoglyph/zero-width/Unicode folds) so an attack clusters by technique, not by spelling: 1gn0r3 pr3v10us and ignore previous land in one cluster instead of re-entering the corpus once per skin.
4. Reproduce. Each canonical primitive runs against your DeploymentConfig × 5 trials.
5. Diff. A separate judge model verdicts each trial; the daily diff ships to Slack, MCP, and the dashboard.

New to the codebase? docs/PROJECT_STRUCTURE.md maps every directory to its pipeline layer and the architecture doc that explains it.

What ROGUE red-teams

ROGUE measures every place a high-stakes AI agent can go wrong: whether the agent can be broken, whether the human oversight around it is meaningful, whether the knowledge it accumulates is safe, whether the AI trusted to strip secrets actually does, whether a deletion it promised actually happened, whether it applies the right safety standard for the person in front of it, and whether the secrets in its private reasoning stay private. Each is scored against an independent, continuously-refreshed standard — the first four backed by a measured result (the last, recipient-boundary, directional), the rest (redaction, deletion, user-context, reasoning) by a wired, runnable instrument whose cross-model numbers await a paid sweep:

• The model. Does a live jailbreak or prompt-injection break your deployment? The daily breach matrix replays open-web attacks against your model × system-prompt, graded by a human-calibrated judge. Finding: most claimed jailbreaks don't even reproduce (Open-Web Jailbreaks Mostly Don't Reproduce in Deployment).
• The human gate. When a person "approves" an AI action, does that approval mean anything? ROGUE measures a reviewer's false-approve rate against an independent answer key — the rubber-stamping failure mode regulators now care about. Early instrument, demonstrated at proof-of-concept scale (n=1) (oversight).
• The agent's memory. Does a shared agent skill-pool leak one user's secrets to the next? ROGUE plants canaries in scrubbed skills and measures recovery: 85% leaked on a weak model despite an explicit never-reveal instruction. Measured on a small canary set (wide, overlapping CIs) — an early result, not a benchmark (A Dead Call Cannot Leak).
• The recipient boundary. Does a shared assistant leak one person's confided secret to a different person in the same chat? It's not toxicity — it's telling the right thing to the wrong recipient, the failure a per-response check never catches. ROGUE has one party confide a canary secret under a norm, another party ask, and checks whether it reaches them. Directional finding on a self-hosted open-weight panel: the smaller the model, the more it leaks — Qwen2.5-7B handed the secret to the wrong party 37% of the time; the 14B and 32B held at 0% — the models people run locally for privacy leak recipient-confidential info most. Directional (4 models, 38 scenarios, deterministic verbatim tier); MuPPET's group-vs-one-on-one delta did not reproduce here (MuPPET / ConfAIde / MAGPIE).
• The reasoning gate. Reasoning models (o1, DeepSeek-R1, QwQ, Claude extended-thinking) think out loud before answering — and that hidden scratchpad leaks what the answer withholds. A model can correctly refuse to hand a user's SSN to a travel site in its reply while spelling the SSN out in its reasoning trace, where a one-line injection can pull it back out. ROGUE captures the trace the rest of the stack never sees (reasoning_content / thinking blocks) and points its whole leak arsenal at it — verbatim, transport-decoded (base64/hex/…), and semantic — flagging reasoning-only leakage: present in the thoughts, absent from the answer. We reproduced it live on DeepSeek-R1 — the answer clean, the SSN sitting in the trace. It's the counterintuitive twist from Leaky Thoughts (2025): cranking a model's reasoning makes its answers safer and its thinking leakier. Wired into the scan; the reasoning-effort × leak-rate board is the paid measurement.
• The user-context gate. Safety isn't one-size-fits-all: "how much of this medication is dangerous?" is informational for a general user but a red flag from someone who's disclosed depression; "recommend cheap liquor in bulk" is harmless until the user says they're in recovery. ROGUE puts a vulnerable-user profile in the deployment's context and fires two matched sets — requests that are harmful for that user (it should decline or redirect) and benign controls (it should still help) — scoring user-specific safety against helpfulness so a deployment can't win by refusing everyone. It's the axis a duty-of-care deployment (health, recovery, minors) lives or dies on, and the one where models fail most: the benchmark this is built on (U-SafeBench, EMNLP 2025) found LLMs act safely for vulnerable users near-never — even with the profile spelled out in front of them (GPT-4o-mini: 1.6%). Instrument wired into the scan; the cross-model board is the paid measurement, not yet a shipped number.
• The deletion gate. When a user invokes their Right to be Forgotten (GDPR Art. 17 / CCPA) and a customer "deletes" their data, is the entity's knowledge actually unrecoverable from the deployed model — or is the deletion a system-prompt band-aid that adversarial paraphrase probing walks straight past? ROGUE audits it black-box: it probes the target for the forgotten entity across many rephrasings (recovery under any one counts, because unlearning is famously format-superficial — 2411.15477), checks recall semantically (revealing the fact, not the exact string), and reports two numbers together — forget-leak-rate (should be zero) and neighbor-retain-rate (an over-aggressive deletion that nukes related knowledge is its own failure — the forget↔retain trade-off of BLUR). It's the auditor's side of machine unlearning, done at deployment time with no access to weights. Instrument wired into the scan; the cross-model board is the paid measurement, not yet a shipped number.
• The redaction gate. When the LLM is the safeguard — a DLP / redaction pipeline scrubbing secrets from a document before it flows downstream — does it remove what the policy requires, without shredding everything else? ROGUE scores that pipeline at the level of propositions — every atomic, inferable fact, not just named entities, because the leak a regex misses is the one you can still infer from what's left — on two opposed axes: security (fraction of policy-sensitive facts actually removed) and utility (fraction of the innocent rest preserved). The trade-off is the whole finding: a redactor that blacks out the page is perfectly secure and perfectly useless, and over-redaction — not leakage — is the failure that dominates in practice. Method after RedacBench (2026); a scored instrument with a stated judge, first cross-model run pending — not yet a headline number.

…and it closes the loop (assurance-native remediation). Finding a breach is half the job. Every scan generates a fix for each breach — a reversible system-prompt patch (the safe, lighter-touch default) and, for the archetypal jailbreak (a user prompt that overrides the system's safety goal), the goal-conditioned preference data to fine-tune the model to hold its system prompt: the recipe from Goal-Conditioned DPO (NAACL 2025), assembled the rigorous way — the target's own refusal and harmful completions, goal-relabeled to teach a system-over-user hierarchy, over-refusal-balanced, and single-model self-generated to sidestep the reward-hacking that stronger-model preference data provably induces. Fine-tuning is the heavier lever — it can affect a model's alignment — so ROGUE marks it the alternative to the reversible patch, and never fine-tunes or deploys anything (you own the runtime). These fixes are suggested; on demand, the deliberate remediation loop re-tests one against the same live corpus to prove it closed the breach without over-blocking (same calibrated judge). Every scan also reports the deployment's instruction-hierarchy robustness — how reliably it holds the system prompt when a user tries to override it — as a first-class defensive number.

One engine, one independent standard, the same operation each time: fire inputs at an AI decision-maker, capture what it does, score it against the standard, emit a reproducible signed record.

Research

ROGUE's findings are written up as papers and posts. PAPERS.md is the index, and each entry links to its preprint plus the code and data in this repo that reproduces it.

• Allocation Is a Capability-Growth Mechanism. In a self-growing red-team, evaluation allocation is a capability lever, not an efficiency layer (8 of 20 starved candidates graduate vs 0 of 20; Fisher p = 0.003). · Zenodo preprint · arXiv on conference acceptance
• Calibrating LLM-as-Judge Breach Detectors. One gate template ("engagement ≠ breach; consummation = breach") calibrates breach judges across classes, validated against human labels four ways. · Zenodo preprint · arXiv on conference acceptance
• Open-Web Jailbreaks Mostly Don't Reproduce in Deployment. Most open-web jailbreaks don't survive as working carriers in deployment context, and a source's claimed rate carries no usable signal (Spearman −0.07). · Zenodo preprint · arXiv on conference acceptance
• A Dead Call Cannot Leak. Canary leakage from shared agent skill pools tracks alignment, not model size. · Zenodo preprint · arXiv on conference acceptance

Deep dives

The mechanics behind the pipeline, each on its own page:

• Scraper-agnostic harvest. A Fetcher registry picks the best backend per capability (page fetch, JS render, search, PDF), so the scraping runs free and keyless out of the box and any scraper or proxy slots in behind one env var — none is a dependency (extraction calls an LLM you choose). Plus a self-tuning ε-greedy bandit that allocates harvest budget by yield (novel primitives per dollar). → docs/harvest-backends.md
• Multimodal red-team. Refused text jailbreaks become real images and audio via deterministic black-box renderers, climbing an autonomous escalation ladder that stops at the first breach. → docs/multimodal.md
• Self-growing attack repertoire. ROGUE harvests reusable techniques, not just payloads, classifying, routing, and graduating / retiring / resurrecting them on live breach evidence, with a governed renderer registry and grammar-driven planning (the planner-willingness finding: 22% → 100% by changing only the planner). → docs/self-growing-repertoire.md
• Judge calibration. Every breach number is an LLM verdict, so the judge is validated against independent human labels four ways: in-distribution FP 2.56%, WildGuardTest harm 88.5%, StrongREJECT −26% inflation, JBB 89.3% human agreement (3rd of 5 field classifiers, tied with the frontier LLM-as-judge baselines, reproducible from data/calibration/), up from a 70.3% v1 judge after a diagnosed recalibration. → docs/judge-calibration.md
• Judge artifact-robustness — the meta-test on the grader. Agreement-with-humans isn't enough: recent work shows a safety judge can be fooled by benign, harm-preserving artifacts — an apology prefix, an appended block of safety boilerplate — that flip a still-harmful response to "safe" without changing what it says. ROGUE ships that meta-test as an artifact-robustness probe (re-grade a labelled slice under those artifacts, report the judge's ΔFNR/ΔFPR) plus a live per-cell disagreement flag that re-grades each breach with a conservative bracket and marks the ones it won't confirm as low-confidence — so a fragile breach is visible, not silently trusted. A $0 replay on the frozen JBB set shows ROGUE's keyless structural judge is robust-by-construction to these tone attacks (it grades structure, not tone); the same probe measures the calibrated LLM judge on demand. Off by default. → docs/research/judge_artifact_robustness.md
• Benchmark, coverage over time. Frozen AdvBench / JBB goal sets run through ROGUE's own graduated ladder against a fixed target, to answer "is this month's ROGUE better than last month's?" (honest caveat: still N=1, pre-recalibration). → docs/benchmark.md
• Dashboard tour. A 5-second pitch and a 5-minute deep-dive: cinematic home, /feed war room (attacks replayed as ATTACKER → MODEL → JUDGE), /matrix breach heatmap, /brief threat brief. → docs/dashboard.md

Capabilities

★ Live in production today — the budget-aware evaluation loop (cutting per-scan cost right now, no quality loss): sequential early-stopping (SPRT) — measured live A/B ~48% fewer model+judge calls at n_trials=12, breach rate unchanged — and the confidence-gated cascade judge — 18–46% fewer paid judge calls at 99.8% agreement. Both are the "Adaptive Budget-Aware Red-Team Evaluation" work and are on in prod. The rest of the budget-control family (survival ranking, pre-fire skip, acquisition ordering) is built + offline-measured but off by default — each awaits its own live A/B (and survival's first live A/B came back negative on a held-out corpus, so it stays off — see its entry).

• 15-family attack taxonomy (OWASP LLM Top 10 + MITRE ATLAS aligned); see docs/taxonomy.md.
• 14-slot payload-template vocabulary for cross-deployment reproduction.
• 15-source open-web harvest list; see docs/sources.md. Not a fixed set: add your own with a ~30-line plugin → docs/adding-sources.md.
• Target panel of 6 production text models (GPT-5.4 Nano, Claude Haiku 4.5, Llama-3.1-8B, Mistral Small, Gemini 3.1 Flash-Lite, Claude Opus 4.8) + 2 audio targets (sampled lighter): cheap-tier models per lab, an open-weight reliability anchor, a frontier reference, and audio endpoints for multimodal coverage.
• Judge-model verdict pipeline (REFUSED / EVADED / PARTIAL_BREACH / FULL_BREACH), human-validated four ways; see Judge calibration.
• Daily threat brief (markdown + JSON) + Slack webhook.
• ROGUE-as-MCP-server: query the attack DB from Claude Desktop / Cursor / Windsurf.
• True multimodal red-team and a self-growing technique repertoire (see Deep dives).
• Deterministic obfuscation augmentation: 10 labelled, zero-LLM-cost transforms (leetspeak, homoglyph, zero-width, fullwidth, zalgo + base64 / ROT13 / hex / Unicode-escape / HTML-entity decode-wraps) skin a defended attack to measure a flip-rate-per-transform, separating "the target pattern-matches the surface string" from "the target understands the technique." A goal-preservation validator (inspired by AdvCodeGen, ICISC'25) checks each mutated variant still expresses the attack's goal before it's fired — decoding obfuscation, comparing to the original — so an over-obfuscated, self-neutered payload isn't miscounted as the model defending, a false negative the naive skin-and-fire loop silently produces.
• Evidence-bank exfiltration detection (inspired by TRACE, 2026): a tool-using agent can smuggle a secret it read from a tool result into a later call's arguments — and ROGUE's deterministic canary check catches a verbatim copy but, by design, not one the agent base64-encodes, hex-dumps, reverses, or paraphrases on the way out. So ROGUE keeps an evidence bank of every secret a tool returned into context, then screens each later call two ways: a decode pass (base64 / hex / ROT13 / unicode-escape / reversed / whitespace-stripped — near-zero-false-positive, the same canary literal, just skinned) and, on demand, a semantic pass (an entailment check that flags a derived leak — "the key's last four are 6789"). It closes the verbatim-substring blind spot (information-flow leakage, in the spirit of GIF / OCELOT) as a non-headline signal — telemetry and hardening, never allowed to inflate the deterministic breach rate. The decode pass runs by default (it's free and matches the exact canary, just skinned); the semantic pass is opt-in (it's an LLM call and an uncalibrated judge).
• Persistent-memory leakage channel (provenance instrumentation, extended). ROGUE extends the same provenance-based leakage instrumentation it runs on tool-call arguments and reasoning traces to a new channel — persistent agent memory — measuring cross-session leakage with the same deterministic framework. A secret an agent stores in one session can be recalled, and leaked, in a later, unrelated one — a boundary a per-response audit never crosses. ROGUE plants an unguessable, HMAC-minted canary as a dormant memory record (a prior session's write), triggers a fresh session on a sensitive topic, and scores whether the agent recalls it (MEMORY_SURFACED) and routes it to a sink (MEMORY_EXFIL) — reusing the existing canary → provenance → trace-judge pipeline, with a new cross-session MEMORY provenance label (the sibling of within-session retrieval). The memory attack itself is prior work (Trojan Hippo); the cross-session instrumentation is ROGUE's. Off by default and byte-identical when off; the $0 deterministic validation proves the instrumentation is sound (a compliant agent leaks, a careful one doesn't, the flag-off path is untouched) — not that real models leak, which the honest boundaries make clear (exact-match store, not embedding/RAG; single-agent, not inter-agent; verbatim-exfil only), so the live cross-model leak rate is a gated two-session measurement, not a shipped number. Grounds on Trojan Hippo (2605.01970) + AgentLeak's C5 channel (2602.11510). → docs/research/memory_exfil_channel.md
• Multi-party contextual-privacy probe (a recipient-norm leakage surface). Every other leakage channel above asks where a secret came from; this one asks the question a group deployment actually fails — to whom may it flow. A shared assistant (a team bot, a household or customer-facing agent) is entrusted with one colleague's confidence — a sensitive personal fact shared under a norm — and then a different participant asks a question whose most helpful answer would give it away. ROGUE embeds an unguessable HMAC canary in that confided fact as a confidential reference, reconstructs the group conversation exactly as MuPPET frames it (the memory in the system prompt, the history and the eliciting question in the user turn), and judges the reply served to the second party on two tiers: a verbatim tier (the canary reappears → the assistant regurgitated the record — deterministic, near-zero-FP, headline) and an opt-in inferable tier (an LLM judge finding the private fact stated-or-implied, MuPPET's real signal — non-headline until it clears calibration). A 38-scenario seed corpus spans seven high-stakes domains (health, legal, finance, HR, academic, immigration, family), with a one_on_one/group switch that reproduces MuPPET's finding that models leak more once an audience is present, and a privacy_instruction toggle that reproduces ConfAIde's sharper one — that they leak even when told not to. It needs no tools, so unlike the memory channel it runs against a live customer chat model directly; off by default and byte-identical when off. The $0 validation proves the plumbing (a blatant assistant regurgitates the confidence, a careful one holds it) — the cross-model multi-party privacy board (which models leak a group member's confidence to the room — MuPPET finds the small open-weights people self-host for privacy leak worst), the one-to-one↔group delta, and the "leaks even when told not to" effect are a gated multi-turn measurement, not a shipped number. Grounds on MuPPET (2606.23217) + ConfAIde (2310.17884) + MAGPIE (2506.20737). → docs/research/multiparty_privacy.md
• Highest-yield encoding operators, with a coverage invariant. ROGUE skins a defended attack in the surface encodings that beat 2026 guardrails and measures which ones flip a refusal back to a breach — now including the four families the literature ranks highest: a nested ROT-13∘Vigenère self-decoding cipher (RoguePrompt, 2511.18790: 84.7% moderation bypass / 71.5% execution on GPT-4o), emoji variation-selector and Unicode-tag smuggling (Bypassing LLM Guardrails, 2504.11168: emoji smuggling hit 100% classifier-evasion and, with tag smuggling, were the only two families to beat a hardened commercial guardrail), and diacritics. Each rides the existing deterministic, $0, cache-stable operator layer — off by default, byte-identical when off — and the reproduction gate decodes each variant so it is proven to still carry the original goal before it fires, never miscounted as a defense. The durable piece is a CI coverage invariant: every evasion family the literature verifies is asserted to map to a live operator and a taxonomy node, so a whole class of attack can never silently fall out of the pipeline. Correctly-decoded round-trip validated at 99.5–100% over 215 real harvested payloads; and the live flip-rate is measured (2026-07-11, gpt-5.4-nano/mini): decode-wrap encodings dominate — HTML-entity 77%, ROT-13 62%, the RoguePrompt ROT-13∘Vigenère cipher 54% — while surface-skin encodings (leetspeak/homoglyph/zero-width/Unicode-tag) flip 0%, so the guardrail isn't string-matching, it's the decode-then-obey behavior that leaks (directional, 2 models). → docs/research/encoding_operators.md
• Domain-jargon evasion augmentation (inspired by ExpGuard, 2026): the semantic sibling of the obfuscation axis. Where the transforms above skin the surface string, this re-expresses a defended attack in the register of a specialist — a board-certified physician, a compliance analyst, counsel — because a guardrail trained on general-harm features often waves through the same request dressed in clinical, regulatory, or statutory jargon. An attacker LLM applies the three empirically-supported levers (technical terminology · authority persona · legitimate-context framing), the deployment's domain rides in its system prompt, and the same goal-preservation gate vetoes any rewrite that quietly sheds the ask, so a flip (refused plain, complied in-register, goal intact) is a real domain-evasion, not an artifact. Wired into the scan (rogue scan --domain-jargon) and the live reproduce sweep; the cross-model flip-rate it feeds — a domain-robustness board — is the paid measurement, not yet a shipped number.
• Judge self-audit for domain blind spots. Because ROGUE's breach judge is itself calibrated on general benchmarks, a probe measures whether it inherits the same weakness it hunts — running the judge over matched general-vs-domain content and reporting the calibration gap (under-detection vs over-flagging, per register). It's the discipline turned inward: we stress-test our own instrument before trusting its numbers on high-stakes deployments. (A first probe found no gap on a frontier judge — the risk surface is smaller, cheaper guard models, which the paid run targets.)
• Survival-ranked reproduction (system-prompt-transfer prediction). A harvested jailbreak that worked in the wild usually doesn't survive being re-hosted under a customer's system prompt — ROGUE's measured cross-config survival base rate is ~14%, so most of a paid reproduce cycle lands on attacks that were dead on arrival. A black-box, embedding-free predictor learns, from an attack's own surface (family/vector, requirements, provenance scores, payload shape) crossed with a cheap descriptor of the target's system-prompt class, the probability it will still breach — and reorders the scan so the likely survivors fire first. Trained free on breach history ROGUE has already paid for; on a group-split-by-primitive back-test it recovers 80% of real survivors after firing only 61% of trials (ROC-AUC 0.77, 4.2× precision lift at the top decile). The honest part is the drift-guard: a newly-harvested or under-evidenced attack family is never deferred — it always gets a real reproduction — which is Kirch et al.'s out-of-distribution-collapse finding (2411.03343) turned into an operating rule, plus a deterministic canary sample that keeps validating the very skips it makes. A black-box predictor of cross-system-prompt survival is the gap the white-box (Kirch, Ball) and per-config (Helm) literature all leave open. ⚠️ Off by default — and its first live A/B (2026-07-11, held-out corpus × config) came back NEGATIVE: at an aggressive skip threshold it fired the 8 highest-survival pairs and recovered 0 of the baseline's 54 breaches — the offline AUC did not transfer to the shifted corpus (the exact OOD-collapse risk the drift-guard exists for, here on a corpus shift). A proper threshold sweep on a larger held-out set is needed before any live budget-saved claim; until then the offline number is on-distribution only. → docs/research/survival_predictor.md
• Pre-fire skip on a calibrated success score (predict-before-fire). Where survival ranking reorders, this decides whether to fire an attack at all: a calibrated probability that a specific harvested attack will breach a specific target, learned from breach history ROGUE already owns, with the attack's payload embedding folded in on top of its structure — so the confident misses can be skipped before a single target or judge call is spent. The engineering is built around making the skip honest: a Platt-calibrated probability you can threshold, a measured recall cost (skip ~40% of trials if you accept missing one breach in five, but only ~7% at a stringent 95% breach recall — the trade-off the prior work leaves unmeasured), the same drift-guard rail (a novel or under-evidenced attack family is never skipped) and a 15% canary that keeps validating the skips. On a group-split back-test the payload embedding adds a small but consistent lift over structure alone (ROC-AUC 0.688 → 0.696), and every skipped attack is surfaced, never silently dropped. The black-box, cross-family, target-conditioned, calibrated skip is a cell the detection (Galinkin), one-family-ranking (Zhang) and white-box (Kirch) literature don't reach. → docs/research/prefire_scorer.md
• Active-learning acquisition ordering (spend the budget on what you'll learn, not just what you'll breach). ROGUE has a fixed paid budget and far more harvested attacks than it can fire; the default fire order is a static, target-blind self-rating that knows nothing about the model in front of it. ROGUE can instead order the budget by an acquisition score that borrows the active-learning playbook — value (the calibrated P(breach) from the pre-fire scorer: attacks likely to succeed here), + uncertainty (attacks whose outcome we genuinely don't know — the ones peaking at a coin-flip probability, exactly where a robustness threshold lives), + diversity (payload-embedding distance from what's already been chosen, so the budget isn't burned on thirty near-identical "ignore previous instructions" clones), + information gain (attacks in an under-sampled model×family cell — a first Bengali many-shot jailbreak teaches more than the tenth DAN variant). It is not a new algorithm — uncertainty/diversity/info-gain are textbook active learning; the work is composing them into one order inside a live red-team, from signals ROGUE already computes, off by default and byte-identical when off. On a leak-free replay over pairs ROGUE has already paid to fire, the acquisition order surfaces ~8 points more breaches and ~14 points more distinct attack families within the first 25% of the budget than the order that ships today (and ~9/~20 points at 50%). The honest part is the attribution: most of the breach-finding gain is the config-aware value signal, while the uncertainty/diversity/info-gain terms are what earn the coverage — which is their purpose (learning the model's safety profile, feeding the long-context robustness board), not raw yield. A greedy maximal-marginal-relevance loop, a drift-guard that never defers a newly-harvested family, and every deferral surfaced rather than silently cut. A first live A/B on a fresh cycle then directionally confirmed the offline result — the acquisition order surfaced more breaches than the shipped order at the same fixed budget — once a modality-compatibility gate stopped it spending budget on media attacks a text-only target can't read; that's a directional confirmation on a narrow panel, not yet the cross-model headline (still off by default). It is the allocation-layer generalization of the pure expected-breach order — it consumes the pre-fire scorer and is a sibling of survival ranking. Grounds on Kossen's Active Testing (2103.05331), Ma's test-selection metrics (1904.13195) and Chung's information-gain acquisition (2405.07440). → docs/research/acquisition_ordering.md
• Sequential early-stopping of the trial loop (SPRT) — ★ LIVE IN PROD. Everyone reports an attack success rate from a fixed handful of trials — but at ROGUE's default n=3 that rate can only be one of {0, ⅓, ⅔, 1}, a number too coarse to mean anything, and paying to fire a large fixed n burns target+judge calls on the many cells that are already obviously safe or obviously broken. ROGUE instead treats the per-cell budget as sequential: each trial's breach/no-breach verdict updates a log-likelihood ratio for the hypothesis test H0 p≤0.25 vs H1 p≥0.55 (Wald's SPRT, α=β=0.05), and the loop stops the instant the ratio crosses a decision boundary — a clearly-safe or clearly-broken cell resolves in ~4–6 trials, while genuinely borderline cells get the full budget, spending the trials exactly where the uncertainty is. Trials fire in small concurrent batches so the sequential test keeps ROGUE's parallelism, and a truncation cap falls back to the incumbent threshold rule so no cell is ever re-graded — only shortcut. Replaying the real test over breach history ROGUE has already paid for, it recovers the same breach verdict for 22% fewer target+judge calls at 99.8% agreement (50–80% on the deep-sampled cells that need the statistics most); and a live two-arm A/B at n_trials=12 (2 real models, 2026-07-11) measured ~48% fewer calls with the breach rate preserved (7.5% → 7.4%) — confirming the replay understated the benefit at depth. It's already active in ROGUE's production scans (ROGUE_SPRT=on), cutting per-scan spend today, with zero new dependencies and off by default in code. Reframing a fixed ASR as a sequential test is, as far as we can find, unreported in the red-team-evaluation literature; it grounds on ConSol's Wald-for-LLMs formulation (2503.17587). → docs/research/sprt_early_stopping.md
• Confidence-gated cascade grading (free tier first, pay only for the ambiguous) — ★ LIVE IN PROD. Every reproduced trial is graded by a safety judge, and on a paid scan that judge is a frontier LLM billed per trial — the top per-trial cost — even for the flat "I can't help with that." refusals that are the safe majority of any red-team run. ROGUE grades each trial with its free, network-free regex judge first and escalates to the calibrated LLM judge only when the cheap tier isn't a confident non-breach. Because that cheap tier costs nothing — it's a regex, not a smaller paid model — every short-circuit removes a full judge call for zero marginal cost, and the LLM judge is spent exactly where the verdict is genuinely ambiguous. The safety rail is an asymmetry: the free tier may stand alone only on a confident non-breach; any breach signal is always re-confirmed by the calibrated judge, so the cheap tier can never inflate the breach rate — every breach still goes through the calibrated judge, and the reported rate matches running the LLM judge on everything to the measured 99.8% agreement, for less money. Replayed $0 over 12,452 already-graded trials, the safe default gate skips 18% of paid judge calls at 99.8% agreement with the calibrated verdict, and a tunable, statistically certified gate (Wilson-floor ≥95% agreement, fixed-sequence-tested à la Jung) reaches 46%. It's the judge-budget control of ROGUE's evaluation loop — the sibling of survival ranking (which attacks) and sequential early-stopping (how many trials) — and it's already active in ROGUE's own production scans, cutting judge spend on every run today. No new dependency, byte-identical when off. Grounds on Ramírez's finding that the cheap tier's own confidence beats a trained router (2405.02134) and Jung's Trust or Escalate threshold calibration (2407.18370). → docs/research/cascade_judge.md
• Judge-shrink decision gate (measure whether a cheaper grader is safe — don't assume it). The safety judge is billed per trial, so a smaller judge model that held agreement would cut the reproduce layer's dominant marginal cost directly — and setting JUDGE_MODEL to something cheap has always been possible. What's dangerous is doing it blind: the classic result (Thakur, Judging the Judges) is that small judges keep ranking models correctly while quietly losing per-verdict fidelity, and they fail by over-firing the positive class — for a red-team, that means over-calling breaches, silently inflating the headline breach matrix. ROGUE ships the honest instrument to decide instead: it reports Cohen's κ (chance-corrected agreement — not raw agreement, which inflates under the 110/300 class balance) plus the false-positive-breach rate with bootstrap confidence intervals, and a candidate judge model ships only if its κ is within tolerance of the frontier baseline and it does not over-call breaches relative to it. The whole A/B runs for $0 by re-aggregating the 300-item public JBB judge-comparison verdicts ROGUE has already logged for ~10 candidate models — no new spend. The finding is sharp and reproduces the literature in ROGUE's own data: on 300 human-labelled items only qwen3-32b clears the gate (κ=0.775 vs Sonnet's 0.814, with a lower false-positive-breach rate), while "bigger" judges are worse — llama-3.3-70b, qwen-2.5-72b and hermes-70b all fall below the 32B model and over-call breaches at 2–3.4× Sonnet's rate; parameter count does not predict judge fidelity. κ earns its keep here: qwen-2.5-72b's 78% raw agreement looks close, but κ=0.577 and a 34% false-positive-breach rate correctly reject it. The build also ships an off-by-default reference-anchoring lever (ROGUE_JUDGE_REFERENCE_K) — Krumdick's "hand the judge a verified reference" fix (No Free Labels), translated to a breach judge that has no per-trial gold answer by injecting human-verified verdict exemplars — whose effect on a small judge is measured by the same gate, never assumed. The production judge stays Sonnet; what ships is the decision discipline (κ + FP-breach + CI, byte-identical when off, no new dependency). Grounds on Krumdick (2503.05061) + Thakur (2406.12624) + Loiseau (2603.29497). → docs/research/judge_shrink_ab.md
• Local-first extraction cascade (free local tier first, pay Haiku only when it can't be trusted). Every harvested document is turned into a structured attack primitive by a per-document LLM call — a real harvest-cost driver. ROGUE tries a cheap local model first and escalates to the Haiku extractor only when the local output is untrustworthy: an abstention, a schema-invalid object, or a payload that isn't grounded in the source document. It is the extraction sibling of the judge cascade, with the same asymmetric rail — the cheap tier can only ever save a Haiku call, it can never drop a document on its own say-so — so quality is never traded for cost. The build ships the enabling wiring (a local/ json-mode provider branch, because the strict structured-output path can't compile the attack-primitive grammar on a local runtime) and a per-field agreement harness that measures, slot by slot, where a local model may actually be trusted before you turn it on. The honest result is a negative one that the mechanism is designed around, and it holds across a whole size sweep: off-the-shelf qwen2.5 at 3B, 14B and 32B all score recall 0/2 on the real attack fixtures — the 3B doesn't even recognize the attacks, while the 14B/32B understand them but won't render the structured payload and ignore an explicit field contract, so scaling the model 15× doesn't help. A naive swap would silently lose attacks at any of these sizes — and neither grounding paper (Lincoln; Bumgardner) even compares against a cheap hosted small model like Haiku, so the literature does not actually establish a Haiku→local win; the real saving needs a fine-tuned local extractor, for which ROGUE's own logged Haiku extractions are the ready distillation set. With a weak local model the cascade simply escalates everything — ≈0 saving, 0 quality loss — which is the correct, safe behaviour. Off by default, byte-identical when off, no new dependency. Grounds on Lincoln (2605.05532) + Bumgardner (2308.01727). → docs/research/extraction_cascade.md
• Noise-corrected breach rates (imperfect-judge de-biasing + certification). A breach rate read straight off an LLM judge is a biased estimate whenever the judge has a non-zero false-positive rate — and the bias points upward exactly in the low-true-rate safety regime, so "the judge flagged 13% of trials" quietly overstates real harm. ROGUE estimates the judge's true- and false-positive rates from the human-labelled calibration 2×2 it already keeps, then de-biases the reported rate (Rogan–Gladen inversion) with a confidence interval that carries both the test-run and the small-calibration-set uncertainty, and stamps a finite-sample certification — "true breach rate < α, with Type-I error ≤ ζ" — that stays valid despite the judge being noisy. Replayed $0 over breach history already paid for, a raw 13.5% judge breach rate de-biases to 1.0% [95% CI 0–6.6%] — under the estimated judge error profile, the large majority of flagged "breaches" are attributable to the judge's own false-positive floor rather than to real harm (a 10%-FPR judge can make a 1%-true-rate system look 10× worse). This is a certification layer for the benchmark — turning a raw judge fraction into a de-biased rate and a certified claim ("true rate < α, Type-I ≤ ζ") — which is what finally lets the previously-uncalibrated redaction / right-to-be-forgotten / user-safety / PII / agent-memory judges ship a headline number. Additive and off by default, so every historical calibration report is untouched, and it refuses rather than fabricates when the judge isn't identifiable (TPR̂ ≤ FPR̂). Grounds on Feng's "Noisy but Valid" (2601.20913) + Lee's LLM-judge reporting method (2511.21140). → docs/research/noise_corrected_calibration.md
• Single-turn consolidation of multi-turn attacks (multi-turn coverage at single-turn cost). A Crescendo-style attack in ROGUE's corpus fires as a real back-and-forth — one victim call per turn — but a multi-turn conversation can be folded into one structured prompt that preserves the sequential progression, and (per Ha et al., One-Shot is Enough, 2503.04856) the consolidated single-turn form is often as potent or more because it slips the harmful sequence past turn-by-turn safeguards that never inspect the whole progression at once. ROGUE renders a multi-turn primitive's turns through one of three deterministic, zero-LLM-cost formatters (bullet-list, numbered, or a Python-list skeleton) and fires it through the ordinary single-invoke path at 1× trial instead of K — a deterministic cost cut a read-only census puts at 71% fewer victim calls across the 8.8% of the corpus that is multi-turn (mean 3.45 turns each). The transform keeps the attack's identity, surfaces every consolidation, and is off by default and byte-identical when off; whether efficacy holds on a live panel is a gated measurement, so the shipped number is the cost, not an efficacy claim. → docs/research/m2s_consolidation.md
• Pre-fire prompt pruning in the escalation search (don't pay to re-fire what you already tried). ROGUE's escalation searcher mutates a refused attack looking for a breaking variant, firing a paid target+judge rollout for every generated candidate — but an attacker exploring a mutation space re-visits near-identical prompts constantly (a redundancy Tree-of-Attacks-with-Pruning names outright). Before a candidate is fired, ROGUE now embeds its prompt and skips the rollout if it is a near-duplicate (cosine ≥ 0.92, the same threshold the harvest deduplicator uses) of one already fired this search — Tree-of-Attacks' prune-before-fire ported to ROGUE's one-child-at-a-time loop — with an optional EvoJail-style diversity reward (S(p)+λ·D(p|P)) that steers the search toward novel prompts without ever dropping one. The measurement is the finding: replayed $0 over ROGUE's real searchers, the flat Thompson bandit — the production-default searcher — re-fires 87.9% exact-duplicate rollouts when it's stuck on a hardened target, versus 7.0% for the tree searcher, and 14.9% of the real harvested prompt corpus are semantic near-duplicates the gate would catch. It is per-search (never a global corpus dedup — a near-duplicate of a different attack tested against a different config can genuinely differ, a confound we name rather than hide), off by default, byte-identical when off, and reuses the existing embedding + cosine + threshold with no new dependency. Cuts the search-expansion budget — a distinct axis from which pairs to fire (survival / pre-fire skip), how many trials (SPRT), and judge economics (cascade). Grounds on TAP (2312.02119) + EvoJail (2605.02921) + KDA (2502.05223). → docs/research/prefire_pruning.md
• Multilingual continuous coverage (the one non-English axis a static benchmark measures once — ROGUE measures it per deployment, continuously). Every red-team benchmark that reports a multilingual number reports it once, statically, on synthetic prompts; the low-resource-language jailbreak is a known-dangerous surface (it broke GPT-4) yet nobody tracks it per-deployment on live-harvested attacks. ROGUE's translate-then-reproduce path expands each harvested attack into its untouched English baseline plus one machine-translated, round-trip-gated variant per target language — a default panel of es / de / ar / ja / bn chosen to span script × resource because the safety gap reverses by regime (low-resource languages are least safe under plain queries, but high-resource languages can be least safe under jailbreaks, since the model understands the adversarial framing well enough to be steered by it) — fires them across the victim panel, and reports the per-deployment English-vs-non-English breach delta. A translated variant persists against its base attack id with the fired language recorded on the result row, so the corpus is never polluted and the delta is a trivial group-by; a variant whose back-translation doesn't preserve meaning is dropped as invalid (never counted as safe or as a breach), and because machine-translation artifacts only ever deflate the low-resource signal, any multilingual breach ROGUE reports is a conservative lower bound. A complementary camouflaged-intent tag flags harvested attacks that hide a harmful ask behind a benign research/DIY/security frame (a benign-frame × dual-use co-occurrence prior — explicitly a weak review flag, since the camouflage literature shows keyword detection can't reliably catch it), and a multilingual harvest source pulls in-the-wild non-English attacks natively. Off by default, byte-identical when off, no new dependency; $0 census over the real corpus quantifies the gap it closes (~99% English-only today), and the cross-model breach-delta headline is one gated paid run away. Grounds on MM-ART (2504.03174) + Atil (2511.00689) + Deng/MultiJail (2310.06474) + Zheng (2509.05471). → docs/research/multilingual_coverage.md
• External benchmark layer against frozen AdvBench / JailbreakBench goal sets.
• Right-to-be-Forgotten deletion audit (entity-unlearning verification, Opt-Out-inspired): probe a deployed model to verify a "deleted" user's knowledge is genuinely unrecoverable — paraphrase/format-robust probing (recovery under any variant = leak), semantic recall (not string match), and a paired forget-leak-rate + neighbor-retain-rate (the same security↔utility discipline). Wired into the scan (rtbf_attributes) and a standalone audit script; the corpus builds on demand from Wikipedia via the terminology miner.
• Redaction / DLP red-team (proposition-level, RedacBench-inspired): the defensive mirror of ROGUE's PII-leakage offense. Decompose a document into atomic inferable propositions → classify each sensitive-per-policy (not a fixed PII list) → check semantic leakage by entailment rather than string match (catches a redaction that masks the entity but leaves the fact recoverable) → report the security ↔ utility trade-off. Fail-soft, no hard LLM dependency (the judge is an injected seam); ships with a RedacBench-style seed corpus (tests/fixtures/redaction/).

Roadmap

• Expand source coverage. More source plugins bring the next ~100 open-web sources online.
• Break bandit. A second, contextual Thompson-sampling bandit that learns how to break (which escalation strategy to try first per attack-family × target); the control surface and reward log are already built and instrumented in prod.
• Red-team multi-party & multi-agent deployments. ROGUE started by red-teaming a single agent over a single-user conversation; the frontier is settings with several parties. The first step — multi-party privacy — is built (off by default, see the capability above): an assistant in a group chat that quietly discloses one member's private data to another, the same audience-conditioned leakage ROGUE already measures one-to-one, scaled to a group where the model has to track who may see what. The remaining frontier is systems of collaborating agents, where a breach must be attributed across the crew — which agent got jailbroken, which one leaked, which one followed the injection another one carried — a genuinely multi-perspective verdict rather than a per-agent one.

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Run it yourself

Everything below is for builders: connecting ROGUE to your tools, running it locally, or driving the pipeline.

Architecture

See docs/architecture.md for the five-layer pipeline diagram and the locked stack table.

MCP integration

ROGUE exposes its threat-intelligence database as a producer-side MCP server: Claude Desktop / Cursor / Windsurf users query the live breach matrix from inside their IDE.

Hosted (recommended, zero setup). The server is mounted into the live API at https://rogue-private.onrender.com/mcp/. Use the Add to Cursor / Add to VS Code buttons on the dashboard home, or add it as a custom connector in Claude Desktop (Settings → Customize → add a custom connector → paste the URL). The hosted server exposes the read-only query tools and the action tools (validate / scan / report / benchmark + Level-3 workflow tools), ~19 in all.

Local (against your own DB), one command:

uv run python scripts/ops/install_mcp.py                  # Claude Desktop (default)
uv run python scripts/ops/install_mcp.py --client cursor  # or: cursor / windsurf

This detects the client's config path, merges in the rogue server entry pointing at your checkout (preserving every other key), and backs up the old file first. It's idempotent; --dry-run previews, --uninstall removes. Then restart the client. Requires a populated DB (run harvest_once.py + reproduce_once.py at least once); the deployed build reads the live Neon DB.

Read-only query tools: query_attacks, query_diff, query_threat_brief, query_breaches_for_config, query_attack_detail, query_worst_attacks. After connecting, ask Claude "What new attacks broke our customer-support config in the last 24 hours?" and it will call query_diff + query_breaches_for_config and summarize.

Transport. Stdio by default (the Claude Desktop path). For remote clients, serve over HTTP:

ROGUE_MCP_TRANSPORT=streamable-http uv run python -m rogue.mcp_server.server
# serves http://127.0.0.1:8001/mcp  (ROGUE_MCP_HOST / ROGUE_MCP_PORT override the bind)

Run the harvest free (keyless scraping)

ROGUE's scraping is free and fully scraper-agnostic — a Fetcher registry picks the best backend per capability, so no scraper is ever a dependency. (The extraction step still calls an LLM you choose — see "what 'free' means, honestly" below.) One command sets up the best free scraper:

rogue setup

That installs crawl4ai plus its Chromium, and that's all most people need: it then auto-leads page fetch + JS render (clean markdown, stealth, unlimited). The harvest is backend-agnostic (a Fetcher registry picks the best backend per capability), so the rest of the free stack slots in automatically with no further config:

Capability	Free backend	How
Page fetch + JS render	crawl4ai	rogue setup (clean markdown, stealth, unlimited)
Web + image search	SearXNG	self-host → SEARXNG_URL (70+ engines, unlimited)
PDF → markdown	local_pdf	always on (pypdf core; rogue setup --pdf upgrades it)
Zero-install fallback	Firecrawl keyless	auto-enabled when nothing else is configured (no account)

Add residential scale with any cheap proxy (Webshare, IPRoyal, your own): one var, applied to all scrapers, ROGUE_PROXY_URL=http://user:pass@host:port. Full matrix + preference order: docs/harvest-backends.md.

Add your own backend. A new backend (the ones above, or anything else: ScrapeGraphAI, context.dev, a house proxy, a paid SERP API) is a single Fetcher file plus one line in the preference order (ROGUE_FETCHER_ORDER). Sources, the harvest pipeline, and the scan never change. PRs welcome.

What "free" means, honestly: the open-web scraping is free. The extraction and the scan judge still call an LLM; point them at any OpenAI-compatible endpoint (including a local Ollama model) to keep the whole loop ~$0.

Bring your own LLM (including a local one)

ROGUE is not locked to one model or API. The target you scan, the extraction step (scraped page → attack primitive), and the judge (grades each response) are each a configurable provider/model: Anthropic, OpenAI, OpenRouter, Groq, Gemini, or an OpenAI-compatible local endpoint:

# Run the judge on a local model (Ollama); the OpenAI SDK honors OPENAI_BASE_URL:
JUDGE_MODEL=openai/llama3.1
OPENAI_BASE_URL=http://localhost:11434/v1
OPENAI_API_KEY=ollama

Honest caveat on a local judge: ROGUE's judge is calibrated, and its verdict credibility (89.3% agreement vs human labels, the κ / SHIP gate in docs/judge-calibration.md) is validated against the default judge. A local/open judge is uncalibrated and tends to under-report breaches until you re-run the calibration harness (scripts/calibration/) against it. Free judge: yes. Trusted judge: re-calibrate first.

Pipeline CLI reference

The two $-billed driver scripts spend LLM credit (plus whatever your chosen scrape backend costs, if any) and write the live DB, so run them deliberately. All flags are optional.

harvest_once.py: harvest → extract → dedup → persist

uv run python scripts/harvest/harvest_once.py --since 1d

Flag	Default	What it does
--since	1d	Harvest window (1d, 14d, 6h).
--x-handles	off	Comma-separated X handles to scrape this run (X is off by default; profile scraping is slow).
--database-url	$DATABASE_URL	Target SQLAlchemy URL.
--extraction-model	Claude Haiku 4.5	Provider-prefixed extraction model (prompt-cached).
--embedding-model	text-embedding-3-small	Embedding model for dedup.

Env toggles: EXTRACTION_CONCURRENCY · HARVEST_INGEST_IMAGES=0 · HARVEST_FOLLOW_LINKS=0. For a single known-fresh URL, use scripts/harvest/harvest_url.py --url "https://x.com/.../status/<id>".

reproduce_once.py: render → target panel → judge → persist

uv run python scripts/reproduce/reproduce_once.py --primitive-limit 50 --judge-batch

Flag	Default	What it does
--primitive-limit N	all	Cap how many primitives are reproduced (top-N by reproducibility_score).
--only-unreproduced	off	Reproduce only primitives with no breach_results yet.
--primitive-ids A,B,…	none	Reproduce exactly the named primitives (overrides other filters).
--n-trials N	5	Trials per (primitive × config), powers the bootstrap CI.
--multimodal-only	off	Only image/audio primitives, rendered as real media.
--persona NAME	off	PAP persona wrap (the B side of the A/B).
--escalate	off	Inline auto-ladder for panel-wide refusals (costly; bound with --escalate-max-spend).
--candidate-quota N	0	Reserve N guaranteed harvested-candidate attempts before early-stop (scheduler policy).
--judge-batch	off	Grade via the Anthropic Batch API (50% off + caching; baseline-only).

scripts/reproduce/candidate_quota_ab.py runs the candidate-quota A/B (the empirical baseline for the break-bandit).

Add your own source

ROGUE's sources are plugins, not a hard-coded list. To harvest from a forum, blog, repo, or feed it doesn't cover yet, write one SourcePlugin subclass: declare a name, a source_type, the required_capabilities it needs to fetch (e.g. UNLOCK for a page, SERP for a search), and an async fetch_since(fetcher, since) that returns RawDocuments. Your plugin owns what the content means; the injected fetcher owns how the bytes arrive. Register it in default_plugins() and the next harvest run extracts, dedupes, and reproduces from it like any built-in. Full walkthrough + a copy-paste example: docs/adding-sources.md.

Repository layout

src/rogue/     # Python package (schemas, harvest, extract, dedupe, reproduce, diff, mcp_server, db, api)
docs/          # architecture, schemas, taxonomy, sources, budget + the deep-dive pages
tests/         # schema round-trip tests + golden fixtures
scripts/       # harvest_once.py, reproduce_once.py, calibration/, ops/
frontend/      # Next.js dashboard

Built by

Benaja Soren Obounou Lekogo Nguia, AI Systems Engineer; previously Grand-Prize winner at Yonsei University for LLM security tooling (GPTFuzz optimization).

License

MIT. See LICENSE.