A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection

Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks. Vulnerability detection is of crucial importance to maintaining the security, integrity, and trustworthiness of software systems. Precise vulnerability detection requires reasoning about the code, making it a good case study for exploring the limits of LLMs' reasoning capabilities. Although recent work has applied LLMs to vulnerability detection using generic prompting techniques, their full capabilities for this task and the types of errors they make when explaining identified vulnerabilities remain unclear. In this paper, we surveyed eleven LLMs that are state-of-the-art in code generation and commonly used as coding assistants, and evaluated their capabilities for vulnerability detection. We systematically searched for the best-performing prompts, incorporating techniques such as in-context learning and chain-of-thought, and proposed three of our own prompting methods. Our results show that while our prompting methods improved the models' performance, LLMs generally struggled with vulnerability detection. They reported 0.5-0.63 Balanced Accuracy and failed to distinguish between buggy and fixed versions of programs in 76% of cases on average. By comprehensively analyzing and categorizing 287 instances of model reasoning, we found that 57% of LLM responses contained errors, and the models frequently predicted incorrect locations of buggy code and misidentified bug types. LLMs only correctly localized 6 out of 27 bugs in DbgBench, and these 6 bugs were predicted correctly by 70-100% of human participants. These findings suggest that despite their potential for other tasks, LLMs may fail to properly comprehend critical code structures and security-related concepts. Our data and code are available at https://figshare.com/s/78fe02e56e09ec49300b.

Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection

Recently, pretrained language models have shown state-of-the-art performance on the vulnerability detection task. These models are pretrained on a large corpus of source code, then fine-tuned on a smaller supervised vulnerability dataset. Due to the different training objectives and the performance of the models, it is interesting to consider whether the models have learned the semantics of code relevant to vulnerability detection, namely bug semantics, and if so, how the alignment to bug semantics relates to model performance. In this paper, we analyze the models using three distinct methods: interpretability tools, attention analysis, and interaction matrix analysis. We compare the models' influential feature sets with the bug semantic features which define the causes of bugs, including buggy paths and Potentially Vulnerable Statements (PVS). We find that (1) better-performing models also aligned better with PVS, (2) the models failed to align strongly to PVS, and (3) the models failed to align at all to buggy paths. Based on our analysis, we developed two annotation methods which highlight the bug semantics inside the model's inputs. We evaluated our approach on four distinct transformer models and four vulnerability datasets and found that our annotations improved the models' performance in the majority of settings - 11 out of 16, with up to 9.57 points improvement in F1 score compared to conventional fine-tuning. We further found that with our annotations, the models aligned up to 232% better to potentially vulnerable statements. Our findings indicate that it is helpful to provide the model with information of the bug semantics, that the model can attend to it, and motivate future work in learning more complex path-based bug semantics. Our code and data are available at https://figshare.com/s/4a16a528d6874aad51a0.

Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation

Software testing is a crucial aspect of software development, and the creation of high-quality tests that adhere to best practices is essential for effective maintenance. Recently, Large Language Models (LLMs) have gained popularity for code generation, including the automated creation of test cases. However, these LLMs are often trained on vast amounts of publicly available code, which may include test cases that do not adhere to best practices and may even contain test smells (anti-patterns). To address this issue, we propose a novel technique called Reinforcement Learning from Static Quality Metrics (RLSQM). To begin, we analyze the anti-patterns generated by the LLM and show that LLMs can generate undesirable test smells. Thus, we train specific reward models for each static quality metric, then utilize Proximal Policy Optimization (PPO) to train models for optimizing a single quality metric at a time. Furthermore, we amalgamate these rewards into a unified reward model aimed at capturing different best practices and quality aspects of tests. By comparing RL-trained models with those trained using supervised learning, we provide insights into how reliably utilize RL to improve test generation quality and into the effects of various training strategies. Our experimental results demonstrate that the RL-optimized model consistently generated high-quality test cases compared to the base LLM, improving the model by up to 21%, and successfully generates nearly 100% syntactically correct code. RLSQM also outperformed GPT-4 on four out of seven metrics. This represents a significant step towards enhancing the overall efficiency and reliability of software testing through Reinforcement Learning and static quality metrics. Our data are available at this link: https://figshare.com/s/ded476c8d4c221222849.

An Empirical Study of Deep Learning Models for Vulnerability Detection

Deep learning (DL) models of code have recently reported great progress for vulnerability detection. In some cases, DL-based models have outperformed static analysis tools. Although many great models have been proposed, we do not yet have a good understanding of these models. This limits the further advancement of model robustness, debugging, and deployment for the vulnerability detection. In this paper, we surveyed and reproduced 9 state-of-the-art (SOTA) deep learning models on 2 widely used vulnerability detection datasets: Devign and MSR. We investigated 6 research questions in three areas, namely model capabilities, training data, and model interpretation. We experimentally demonstrated the variability between different runs of a model and the low agreement among different models' outputs. We investigated models trained for specific types of vulnerabilities compared to a model that is trained on all the vulnerabilities at once. We explored the types of programs DL may consider "hard" to handle. We investigated the relations of training data sizes and training data composition with model performance. Finally, we studied model interpretations and analyzed important features that the models used to make predictions. We believe that our findings can help better understand model results, provide guidance on preparing training data, and improve the robustness of the models. All of our datasets, code, and results are available at https://figshare.com/s/284abfba67dba448fdc2.

DeepDFA: Dataflow Analysis-Guided Efficient Graph Learning for Vulnerability Detection

Deep learning-based vulnerability detection models have recently been shown to be effective and, in some cases, outperform static analysis tools. However, the highest-performing approaches use token-based transformer models, which do not leverage domain knowledge. Classical program analysis techniques such as dataflow analysis can detect many types of bugs and are the most commonly used methods in practice. Motivated by the causal relationship between bugs and dataflow analysis, we present DeepDFA, a dataflow analysis-guided graph learning framework and embedding that uses program semantic features for vulnerability detection. We show that DeepDFA is performant and efficient. DeepDFA ranked first in recall, first in generalizing over unseen projects, and second in F1 among all the state-of-the-art models we experimented with. It is also the smallest model in terms of the number of parameters, and was trained in 9 minutes, 69x faster than the highest-performing baseline. DeepDFA can be used with other models. By integrating LineVul and DeepDFA, we achieved the best vulnerability detection performance of 96.4 F1 score, 98.69 precision, and 94.22 recall.