REVIEW 8 cited by
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
read the original abstract
While automated vulnerability detection techniques have made promising progress in detecting security vulnerabilities, their scalability and applicability remain challenging. The remarkable performance of Large Language Models (LLMs), such as GPT-4 and CodeLlama, on code-related tasks has prompted recent works to explore if LLMs can be used to detect vulnerabilities. In this paper, we perform a more comprehensive study by concurrently examining a higher number of datasets, languages and LLMs, and qualitatively evaluating performance across prompts and vulnerability classes while addressing the shortcomings of existing tools. Concretely, we evaluate the effectiveness of 16 pre-trained LLMs on 5,000 code samples from five diverse security datasets. These balanced datasets encompass both synthetic and real-world projects in Java and C/C++ and cover 25 distinct vulnerability classes. Overall, LLMs across all scales and families show modest effectiveness in detecting vulnerabilities, obtaining an average accuracy of 62.8% and F1 score of 0.71 across datasets. They are significantly better at detecting vulnerabilities only requiring intra-procedural analysis, such as OS Command Injection and NULL Pointer Dereference. Moreover, they report higher accuracies on these vulnerabilities than popular static analysis tools, such as CodeQL. 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). Interestingly, we observe that LLMs show promising abilities at performing parts of the analysis correctly, such as identifying vulnerability-related specifications and leveraging natural language information to understand code behavior (e.g., to check if code is sanitized). We expect our insights to guide future work on LLM-augmented vulnerability detection systems.
Forward citations
Cited by 8 Pith papers
-
Longitudinal Analyses of SAST Tools: A CodeQL Case Study
CodeQL detected 171 CVEs total, with 83 caught by a prior version before the fix; detections were often actionable within the vulnerable file but not stable across tool versions.
-
QASecClaw: A Multi-Agent LLM Approach for False Positive Reduction in Static Application Security Testing
A multi-agent LLM system cuts false positives in static application security testing by 88.6% on the OWASP Benchmark while dropping recall by only 3.1%.
-
RubberDuckBench: A Benchmark for AI Coding Assistants
RubberDuckBench shows top AI models score around 68% on real GitHub coding questions, rarely answer completely correctly, and hallucinate in 58% of responses on average.
-
MetaLint: Easy-to-Hard Generalization for Code Linting
MetaLint uses meta-learning to let models generalize from easy synthetic linting data to hard human-curated best practices, yielding large F-score gains on a new PEP-inspired benchmark.
-
Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points
ML4AVD research remains locked into binary function-level classification of C/C++ vulnerabilities because twelve pain points in the pipeline reinforce each other through feedback loops.
-
QuiLL: An LLM-Based Vulnerability Assessment Framework for the Wild
QuiLL is a new evaluation pipeline that uses optimized LLM prompts, dynamic in-context learning from an NVD vector store, and a novel accuracy-plus-reasoning metric to benchmark vulnerability detection in real code.
-
Fine-Tuning Code Language Models to Detect Cross-Language Bugs
Fine-tuning 13 CodeLMs on a constructed CLB dataset with nine interaction types improves detection, with UniXcoder-base reaching F1 0.7407 and small models outperforming large ones.
-
OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing
OpenAnt is an open-source pipeline that uses code decomposition, LLM-based adversarial verification, and automated dynamic testing to find vulnerabilities in large projects like OpenSSL and WordPress while claiming lo...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.