SecCodePLT: A Unified Platform for Evaluating the Security of Code GenAI (2410.11096v1)

Abstract: Existing works have established multiple benchmarks to highlight the security risks associated with Code GenAI. These risks are primarily reflected in two areas: a model potential to generate insecure code (insecure coding) and its utility in cyberattacks (cyberattack helpfulness). While these benchmarks have made significant strides, there remain opportunities for further improvement. For instance, many current benchmarks tend to focus more on a model ability to provide attack suggestions rather than its capacity to generate executable attacks. Additionally, most benchmarks rely heavily on static evaluation metrics, which may not be as precise as dynamic metrics such as passing test cases. Conversely, expert-verified benchmarks, while offering high-quality data, often operate at a smaller scale. To address these gaps, we develop SecCodePLT, a unified and comprehensive evaluation platform for code GenAIs' risks. For insecure code, we introduce a new methodology for data creation that combines experts with automatic generation. Our methodology ensures the data quality while enabling large-scale generation. We also associate samples with test cases to conduct code-related dynamic evaluation. For cyberattack helpfulness, we set up a real environment and construct samples to prompt a model to generate actual attacks, along with dynamic metrics in our environment. We conduct extensive experiments and show that SecCodePLT outperforms the state-of-the-art (SOTA) benchmark CyberSecEval in security relevance. Furthermore, it better identifies the security risks of SOTA models in insecure coding and cyberattack helpfulness. Finally, we apply SecCodePLT to the SOTA code agent, Cursor, and, for the first time, identify non-trivial security risks in this advanced coding agent.

• The paper presents a unified security framework, SecCodePLT, that benchmarks Code GenAI risks by measuring insecure code generation and cyberattack strategies.
• It employs a two-tiered data creation process combining expert insight with automated generation to ensure high-quality, scalable evaluation.
• Experimental results reveal near-perfect security relevance and instruction faithfulness scores, emphasizing the urgent need for enhanced safety measures.

Evaluation of Security Risks in Code Generative AI: An Analysis of SecCodePLT

The paper "SecCodePLT: A Unified Platform for Evaluating the Security of Code GenAI" introduces a comprehensive framework for assessing the security implications of code generative AI systems. In particular, the paper emphasizes two major risks associated with Code GenAI: the potential for generating insecure code and its utility in facilitating cyberattacks. The authors present SecCodePLT as an improved benchmarking tool, addressing limitations found in existing platforms like CyberSecEval.

SecCodePLT comprises two main components: evaluating insecure code generation and assessing cyberattack facilitation capabilities. For insecure coding, the paper details a two-tiered data creation approach combining expert input with automated generation. This methodology strives to balance data quality with scalable production, ensuring the data is both extensive and relevant to security scenarios. By introducing test cases and hybrid metrics for dynamic evaluation, the authors aim to achieve a more precise measurement of a model's propensity to produce insecure code.

On the other hand, the platform's cyberattack evaluation integrates a real-world test environment, dynamically examining generated attack outputs against predefined scenarios structured by MITRE ATT&CK. This aspect of SecCodePLT is aimed at measuring a model's capability to craft executable attack strategies, which prior benchmarks have inadequately addressed.

In experimental evaluations, SecCodePLT demonstrated superiority over CyberSecEval, notably through its nearly perfect security relevance and instruction faithfulness scores. Its dynamic metrics allowed for a more detailed and accurate identification of security risks in state-of-the-art models, such as GPT-4o and Claude, revealing a significant need for improved safety features among popular code GenAI models.

Practical implications of the research are evident in the need for tighter integration of safety measures in AI-assisted code generation, especially given the found potential for these models to generate both insecure code and facilitate cyberattacks. Moreover, the paper highlights a consistent demand for larger, scalable benchmarks that reflect real-world security vulnerabilities comprehensively.

Theoretically, this paper advances the understanding of security evaluation in AI systems by providing a more robust analytical tool that addresses the limitations of static evaluation methods. These insights drive further developments in both defensive AI modeling and the alignment of generative models with secure coding practices.

Future research directions might look into extending these methodologies across more diverse programming languages and security domains. Additionally, the integration of these evaluation tools with adaptive learning systems could allow for continuous model improvement based on real-time security evaluations.

In conclusion, the paper presents SecCodePLT as a significant contribution to the ongoing efforts in evaluating and mitigating the security risks associated with generative AI systems in code development, offering both immediate and long-term value to researchers and practitioners in the cybersecurity and AI fields.