* update 2023-12-21 06:16:42

arXiv_db/Malware/2023.md

@@ -3472,6 +3472,18 @@
 
 <details>
 
+<summary>2023-12-12 14:22:20 - Maatphor: Automated Variant Analysis for Prompt Injection Attacks</summary>
+
+- *Ahmed Salem, Andrew Paverd, Boris Köpf*
+
+- `2312.11513v1` - [abs](http://arxiv.org/abs/2312.11513v1) - [pdf](http://arxiv.org/pdf/2312.11513v1)
+
+> Prompt injection has emerged as a serious security threat to large language models (LLMs). At present, the current best-practice for defending against newly-discovered prompt injection techniques is to add additional guardrails to the system (e.g., by updating the system prompt or using classifiers on the input and/or output of the model.) However, in the same way that variants of a piece of malware are created to evade anti-virus software, variants of a prompt injection can be created to evade the LLM's guardrails. Ideally, when a new prompt injection technique is discovered, candidate defenses should be tested not only against the successful prompt injection, but also against possible variants.   In this work, we present, a tool to assist defenders in performing automated variant analysis of known prompt injection attacks. This involves solving two main challenges: (1) automatically generating variants of a given prompt according, and (2) automatically determining whether a variant was effective based only on the output of the model. This tool can also assist in generating datasets for jailbreak and prompt injection attacks, thus overcoming the scarcity of data in this domain.   We evaluate Maatphor on three different types of prompt injection tasks. Starting from an ineffective (0%) seed prompt, Maatphor consistently generates variants that are at least 60% effective within the first 40 iterations.
+
+</details>
+
+<details>
+
 <summary>2023-12-13 12:36:03 - Security aspects in Smart Meters: Analysis and Prevention</summary>
 
 - *Rebeca P. Díaz Redondo, Ana Fernández Vilas, Gabriel Fernández dos Reis*
@@ -3506,3 +3518,27 @@
 
 </details>
 
+<details>
+
+<summary>2023-12-17 11:07:31 - Android Malware Detection with Unbiased Confidence Guarantees</summary>
+
+- *Harris Papadopoulos, Nestoras Georgiou, Charalambos Eliades, Andreas Konstantinidis*
+
+- `2312.11559v1` - [abs](http://arxiv.org/abs/2312.11559v1) - [pdf](http://arxiv.org/pdf/2312.11559v1)
+
+> The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies examine Machine Learning techniques, as the most promising approach for mobile malware detection, without however quantifying the uncertainty involved in their detections. In this paper, we address this problem by proposing a machine learning dynamic analysis approach that provides provably valid confidence guarantees in each malware detection. Moreover the particular guarantees hold for both the malicious and benign classes independently and are unaffected by any bias in the data. The proposed approach is based on a novel machine learning framework, called Conformal Prediction, combined with a random forests classifier. We examine its performance on a large-scale dataset collected by installing 1866 malicious and 4816 benign applications on a real android device. We make this collection of dynamic analysis data available to the research community. The obtained experimental results demonstrate the empirical validity, usefulness and unbiased nature of the outputs produced by the proposed approach.
+
+</details>
+
+<details>
+
+<summary>2023-12-19 13:48:58 - Towards an in-depth detection of malware using distributed QCNN</summary>
+
+- *Tony Quertier, Grégoire Barrué*
+
+- `2312.12161v1` - [abs](http://arxiv.org/abs/2312.12161v1) - [pdf](http://arxiv.org/pdf/2312.12161v1)
+
+> Malware detection is an important topic of current cybersecurity, and Machine Learning appears to be one of the main considered solutions even if certain problems to generalize to new malware remain. In the aim of exploring the potential of quantum machine learning on this domain, our previous work showed that quantum neural networks do not perform well on image-based malware detection when using a few qubits. In order to enhance the performances of our quantum algorithms for malware detection using images, without increasing the resources needed in terms of qubits, we implement a new preprocessing of our dataset using Grayscale method, and we couple it with a model composed of five distributed quantum convolutional networks and a scoring function. We get an increase of around 20 \% of our results, both on the accuracy of the test and its F1-score.
+
+</details>
+