LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning

• URL: http://arxiv.org/abs/2401.16185v1
• Date: Mon, 29 Jan 2024 14:32:27 GMT
• Title: LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
• Authors: Yuqiang Sun and Daoyuan Wu and Yue Xue and Han Liu and Wei Ma and Lyuye Zhang and Miaolei Shi and Yang Liu
• Abstract summary: Large language models (LLMs) have demonstrated significant poten- tial for many downstream tasks, including vulnerability detection. Recent attempts to use LLMs for vulnerability detection are prelim- inary, as they lack an in-depth understanding of a subject LLM's vulnerability reasoning capability. We propose a unified evaluation framework named LLM4Vuln, which separates LLMs' vulnerability reasoning from their other capabilities.
• Score: 18.025174693883788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated significant poten- tial for many downstream tasks, including those requiring human- level intelligence, such as vulnerability detection. However, recent attempts to use LLMs for vulnerability detection are still prelim- inary, as they lack an in-depth understanding of a subject LLM's vulnerability reasoning capability - whether it originates from the model itself or from external assistance, such as invoking tool sup- port and retrieving vulnerability knowledge. In this paper, we aim to decouple LLMs' vulnerability reason- ing capability from their other capabilities, including the ability to actively seek additional information (e.g., via function calling in SOTA models), adopt relevant vulnerability knowledge (e.g., via vector-based matching and retrieval), and follow instructions to out- put structured results. To this end, we propose a unified evaluation framework named LLM4Vuln, which separates LLMs' vulnerability reasoning from their other capabilities and evaluates how LLMs' vulnerability reasoning could be enhanced when combined with the enhancement of other capabilities. To demonstrate the effectiveness of LLM4Vuln, we have designed controlled experiments using 75 ground-truth smart contract vulnerabilities, which were extensively audited as high-risk on Code4rena from August to November 2023, and tested them in 4,950 different scenarios across three represen- tative LLMs (GPT-4, Mixtral, and Code Llama). Our results not only reveal ten findings regarding the varying effects of knowledge en- hancement, context supplementation, prompt schemes, and models but also enable us to identify 9 zero-day vulnerabilities in two pilot bug bounty programs with over 1,000 USD being awarded.

Related papers

• Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation [88.78166077081912]
  We introduce a multimodal unlearning benchmark, UnLOK-VQA, and an attack-and-defense framework to evaluate methods for deleting specific multimodal knowledge from MLLMs.<n>Our results show multimodal attacks outperform text- or image-only ones, and that the most effective defense removes answer information from internal model states.
  arXiv  Detail & Related papers  (2025-05-01T01:54:00Z)
• Do LLMs Consider Security? An Empirical Study on Responses to Programming Questions [10.69738882390809]
  ChatGPT can volunteer context-specific information to developers, promoting safe coding practices. We evaluate the degree of security awareness exhibited by three prominent LLMs: Claude 3, GPT-4, and Llama 3. Our findings show that all three models struggle to accurately detect and warn users about vulnerabilities, achieving a detection rate of only 12.6% to 40% across our datasets.
  arXiv  Detail & Related papers  (2025-02-20T02:20:06Z)
• Code Change Intention, Development Artifact and History Vulnerability: Putting Them Together for Vulnerability Fix Detection by LLM [13.278153690972243]
  VulFixMiner and CoLeFunDa focus solely on code changes, neglecting essential context from development artifacts. We propose LLM4VFD, a novel framework that leverages Large Language Models (LLMs) enhanced with Chain-of-Thought reasoning and In-Context Learning.
  arXiv  Detail & Related papers  (2025-01-24T23:40:03Z)
• Iterative Self-Tuning LLMs for Enhanced Jailbreaking Capabilities [63.603861880022954]
  We introduce ADV-LLM, an iterative self-tuning process that crafts adversarial LLMs with enhanced jailbreak ability. Our framework significantly reduces the computational cost of generating adversarial suffixes while achieving nearly 100% ASR on various open-source LLMs. It exhibits strong attack transferability to closed-source models, achieving 99% ASR on GPT-3.5 and 49% ASR on GPT-4, despite being optimized solely on Llama3.
  arXiv  Detail & Related papers  (2024-10-24T06:36:12Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input. GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• Can LLMs be Fooled? Investigating Vulnerabilities in LLMs [4.927763944523323]
  The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP) This paper will synthesize the findings from each vulnerability section and propose new directions of research and development. By understanding the focal points of current vulnerabilities, we can better anticipate and mitigate future risks.
  arXiv  Detail & Related papers  (2024-07-30T04:08:00Z)
• Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
  This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T15:31:26Z)
• AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
  Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses. Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies. We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
  arXiv  Detail & Related papers  (2024-06-24T15:16:45Z)
• SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors [64.9938658716425]
  Existing evaluations of large language models' (LLMs) ability to recognize and reject unsafe user requests face three limitations. First, existing methods often use coarse-grained of unsafe topics, and are over-representing some fine-grained topics. Second, linguistic characteristics and formatting of prompts are often overlooked, like different languages, dialects, and more -- which are only implicitly considered in many evaluations. Third, existing evaluations rely on large LLMs for evaluation, which can be expensive.
  arXiv  Detail & Related papers  (2024-06-20T17:56:07Z)
• Towards Effectively Detecting and Explaining Vulnerabilities Using Large Language Models [17.96542494363619]
  Large language models (LLMs) have demonstrated remarkable capabilities in comprehending complex contexts. In this paper, we conduct a study to investigate the capabilities of LLMs in both detecting and explaining vulnerabilities. Under specialized fine-tuning for vulnerability explanation, our LLMVulExp not only detects the types of vulnerabilities in the code but also analyzes the code context to generate the cause, location, and repair suggestions.
  arXiv  Detail & Related papers  (2024-06-14T04:01:25Z)
• Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning [61.2224355547598]
  Open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. Our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions.
  arXiv  Detail & Related papers  (2024-04-16T13:22:54Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models [79.0183835295533]
  We introduce the first benchmark for indirect prompt injection attacks, named BIPIA, to assess the risk of such vulnerabilities. Our analysis identifies two key factors contributing to their success: LLMs' inability to distinguish between informational context and actionable instructions, and their lack of awareness in avoiding the execution of instructions within external content. We propose two novel defense mechanisms-boundary awareness and explicit reminder-to address these vulnerabilities in both black-box and white-box settings.
  arXiv  Detail & Related papers  (2023-12-21T01:08:39Z)
• How Far Have We Gone in Vulnerability Detection Using Large Language Models [15.09461331135668]
  We introduce a comprehensive vulnerability benchmark VulBench. This benchmark aggregates high-quality data from a wide range of CTF challenges and real-world applications. We find that several LLMs outperform traditional deep learning approaches in vulnerability detection.
  arXiv  Detail & Related papers  (2023-11-21T08:20:39Z)
• Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities [12.82645410161464]
  We evaluate the effectiveness of 16 pre-trained Large Language Models on 5,000 code samples from five diverse security datasets. Overall, LLMs show modest effectiveness in detecting vulnerabilities, obtaining an average accuracy of 62.8% and F1 score of 0.71 across datasets. We find that advanced prompting strategies that involve step-by-step analysis significantly improve performance of LLMs on real-world datasets in terms of F1 score (by upto 0.18 on average)
  arXiv  Detail & Related papers  (2023-11-16T13:17:20Z)
• LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks [0.0]
  We explore the intersection of Language Models (LLMs) and penetration testing. We introduce a fully automated privilege-escalation tool for evaluating the efficacy of LLMs for (ethical) hacking. We analyze the impact of different context sizes, in-context learning, optional high-level mechanisms, and memory management techniques.
  arXiv  Detail & Related papers  (2023-10-17T17:15:41Z)
• Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs [59.596335292426105]
  This paper collects the first open-source dataset to evaluate safeguards in large language models. We train several BERT-like classifiers to achieve results comparable with GPT-4 on automatic safety evaluation.
  arXiv  Detail & Related papers  (2023-08-25T14:02:12Z)
• Can Large Language Models Find And Fix Vulnerable Software? [0.0]
  GPT-4 identified approximately four times the vulnerabilities than its counterparts. It provided viable fixes for each vulnerability, demonstrating a low rate of false positives. GPT-4's code corrections led to a 90% reduction in vulnerabilities, requiring only an 11% increase in code lines.
  arXiv  Detail & Related papers  (2023-08-20T19:33:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.