Create an AI system that assists security researchers in discovering new vulnerabilities:
- Pattern Discovery: Discover new vulnerability patterns
- Research Automation: Automate repetitive research tasks
- Hypothesis Generation: Generate hypotheses about potential vulnerabilities
- Literature Review: Automate literature review and research synthesis
- Experiment Design: Help design security experiments
- Data Analysis: Analyze research data and identify patterns
- Report Generation: Generate research reports and papers
- Collaboration: Facilitate collaboration among researchers
Requirements:
- Build pattern discovery algorithms
- Implement research task automation
- Create hypothesis generation using ML
- Build literature review automation
- Implement experiment design assistance
- Create research data analysis tools
- Build report generation for research
- Implement collaboration features
- Create integration with research databases
- Build knowledge graph of security research
Acceptance Criteria:
- Discover at least 5 new vulnerability patterns
- Automate 80%+ of repetitive research tasks
- Hypothesis generation improves research efficiency by 40%
- Literature review reduces research time by 50%
- Research reports are publication-quality
- Collaboration features improve researcher productivity
- Integration with existing research tools
Difficulty: Critical - Requires security research expertise, ML for research automation
Maintainer Scope Upgrade
This issue is being expanded into a substantial, production-quality ChainProof enhancement. The implementation should be designed as maintainable platform work, not as a narrow proof of concept. A successful pull request must provide a cohesive user-facing capability, typed internal APIs, robust tests, documentation, and CI-safe behavior.
Expanded Objective
Develop a security research assistant that helps discover, evaluate, and document new vulnerability classes. The feature should fit the existing monorepo architecture, reuse current scanner/report/CLI patterns where appropriate, and avoid introducing ad hoc subsystems that are difficult to test or maintain.
Required Implementation Depth
This issue is intentionally scoped to require more than 700 lines of meaningful implementation work. The line count expectation applies to purposeful source, tests, fixtures, and documentation that are necessary to deliver the feature. It must not be satisfied through generated output, lockfile churn, formatting-only changes, duplicated boilerplate, or artificial padding.
Expected work includes:
- Research workflows for hypothesis generation, pattern extraction, literature synthesis, and experiment planning.
- Structured outputs that feed new ChainProof rule proposals and evaluation datasets.
- Tests/mocks that keep the workflow deterministic when external LLM or web providers are unavailable.
- Public or internal types/interfaces where they clarify behavior and reduce future integration risk.
- Failure-mode handling for invalid input, missing configuration, unavailable optional dependencies, and degraded execution paths.
- Documentation updates that explain how maintainers and users should operate the new capability in local and CI environments.
Professional Quality Bar
The implementation must be production-ready and reviewable in isolation. Contributors should include clear separation between parsing, analysis, reporting, CLI/action integration, and persistence or provider code where those concerns apply. The code should follow existing ChainProof conventions, keep behavior deterministic in tests, and avoid coupling core analysis to network-only services unless explicitly optional and mocked.
Acceptance Criteria
- The delivered PR contains more than 700 meaningful lines of implementation across source, tests, fixtures, and docs, excluding generated files and lockfile-only changes.
- The feature is integrated into the relevant package entrypoints, CLI commands, report formats, GitHub Action behavior, or documentation as appropriate for this issue.
- Unit tests cover normal operation, edge cases, invalid inputs, and at least one realistic fixture or end-to-end workflow.
- Any optional external service, model provider, database, or platform integration has deterministic mocks or fallbacks so CI does not depend on secrets or network availability.
- User-facing output is documented and stable enough for downstream automation.
- Backward compatibility is preserved unless the PR explicitly documents a migration path and the maintainer approves it.
Mandatory CI and Merge Requirements
A PR resolving this issue must not be merged until all repository CI checks pass. At minimum, reviewers should verify the following from a clean checkout:
npm cinpm run lintnpm run build --workspaces --if-presentnpm run test:ci --workspace=packages/corenpm test --workspaces --if-presentnpm run build --workspace=packages/core && npm run docs --workspace=packages/core- Any package-specific tests, examples, validators, or integration checks introduced by the PR
If the PR adds a GitHub Action, report format, dashboard, persistence layer, or external integration, it must also include CI coverage or a documented local verification command for that path. Known warnings are acceptable only when they are documented and do not hide failures.
Review Expectations
Reviewers should reject PRs that only stub APIs, add superficial wrappers, omit tests for critical behavior, rely on live secrets in CI, or meet the line-count target through non-functional bulk changes. The preferred solution is a focused but complete vertical slice that leaves ChainProof more reliable, easier to operate, and easier to extend.
Create an AI system that assists security researchers in discovering new vulnerabilities:
Requirements:
Acceptance Criteria:
Difficulty: Critical - Requires security research expertise, ML for research automation
Maintainer Scope Upgrade
This issue is being expanded into a substantial, production-quality ChainProof enhancement. The implementation should be designed as maintainable platform work, not as a narrow proof of concept. A successful pull request must provide a cohesive user-facing capability, typed internal APIs, robust tests, documentation, and CI-safe behavior.
Expanded Objective
Develop a security research assistant that helps discover, evaluate, and document new vulnerability classes. The feature should fit the existing monorepo architecture, reuse current scanner/report/CLI patterns where appropriate, and avoid introducing ad hoc subsystems that are difficult to test or maintain.
Required Implementation Depth
This issue is intentionally scoped to require more than 700 lines of meaningful implementation work. The line count expectation applies to purposeful source, tests, fixtures, and documentation that are necessary to deliver the feature. It must not be satisfied through generated output, lockfile churn, formatting-only changes, duplicated boilerplate, or artificial padding.
Expected work includes:
Professional Quality Bar
The implementation must be production-ready and reviewable in isolation. Contributors should include clear separation between parsing, analysis, reporting, CLI/action integration, and persistence or provider code where those concerns apply. The code should follow existing ChainProof conventions, keep behavior deterministic in tests, and avoid coupling core analysis to network-only services unless explicitly optional and mocked.
Acceptance Criteria
Mandatory CI and Merge Requirements
A PR resolving this issue must not be merged until all repository CI checks pass. At minimum, reviewers should verify the following from a clean checkout:
npm cinpm run lintnpm run build --workspaces --if-presentnpm run test:ci --workspace=packages/corenpm test --workspaces --if-presentnpm run build --workspace=packages/core && npm run docs --workspace=packages/coreIf the PR adds a GitHub Action, report format, dashboard, persistence layer, or external integration, it must also include CI coverage or a documented local verification command for that path. Known warnings are acceptable only when they are documented and do not hide failures.
Review Expectations
Reviewers should reject PRs that only stub APIs, add superficial wrappers, omit tests for critical behavior, rely on live secrets in CI, or meet the line-count target through non-functional bulk changes. The preferred solution is a focused but complete vertical slice that leaves ChainProof more reliable, easier to operate, and easier to extend.