The use of large-scale language models (LLMs) is expanding, creating new cybersecurity risks. These risks arise from its core characteristics, including enhanced code generation capabilities, enhanced deployment for real-time code generation, automatic execution within a code interpreter, and integration into applications that process untrusted data. This requires a robust mechanism for cybersecurity assessment.
Previous studies to evaluate the security properties of LLM include open benchmark frameworks and position papers proposing evaluation criteria. CyberMetric, SecQA, and WMDP-Cyber use a multiple-choice format similar to educational assessments. While CyberBench extends assessment to a variety of tasks within the cybersecurity domain, LLM4Vuln focuses on vulnerability discovery and connects LLM with external knowledge. CYBERSECEVAL 1's application, Rainbow Teaming, automatically generates adversarial prompts similar to those used in cyberattack tests.
Meta researchers present Cyberseval 2, Benchmarks to assess LLM security risks and functionality, including prompt injection and code interpreter abuse tests. The benchmark's open source code facilitates the evaluation of other LLMs. This paper also introduces a trade-off between safety and utility, quantified by the false rejection rate (FRR), where an LLM's tendency to reject both dangerous and benign prompts impacts utility. I am emphasizing that. A robust test set evaluates FRR for cyber-attack usability risk and reveals LLM's ability to handle borderline requests while rejecting the most risky requests.
CyberSecEval 2 categorizes prompted injection evaluation tests into logic violation types and security violation types, covering a wide range of injection strategies. Vulnerability exploitation testing avoids LLM memorization and targets LLM's general reasoning abilities, focusing on difficult but solvable scenarios. Code interpreter malpractice evaluation prioritizes LLM conditioning alongside specific malpractice categories, and LLM examiners evaluate the compliance of the generated code. This approach ensures a comprehensive assessment of LLM security across prompt injection, vulnerability exploitation, and interpreter exploitation, facilitating robustness of LLM development and risk assessment.
CyberSecEval 2 testing revealed increased awareness of security concerns, with LLM compliance rates for cyber attack assistance requests dropping from 52% to 28%. Non-code specific models like Llama 3 showed higher non-conformity rates, while CodeLlama-70b-Instruct approached state-of-the-art performance. The FRR evaluation revealed variations, with “codeLlama-70B” showing a significantly higher FRR. Immediate injection tests demonstrated the vulnerability of the LLM, with all models failing injection attempts at a rate greater than 17.1%. Code exploitation and interpreter exploitation testing highlighted the limitations of LLM and highlighted the need for enhanced security measures.
The main contributions of this study are:
- The researchers added a robust prompt injection test that evaluates LLM's 15 attack categories.
- They introduced an assessment to measure LLM compliance with instructions aimed at compromising the attached code interpreter.
- Contains an evaluation suite that measures LLM capabilities when writing exploits in C, Python, and JavaScript, covering logic vulnerabilities, memory exploits, and SQL injection.
- We introduced a new dataset to evaluate LLM FRR when asked to perform cybersecurity tasks and demonstrated the trade-off between usefulness and harm.
In conclusion, this study presents CYBERSECEVAL 2, a comprehensive benchmark suite for assessing LLM cybersecurity risks. Prompt injection vulnerabilities remain present in all models tested (13% to 47% success rate), highlighting the need for hardened guardrails. Measuring the fraudulent rejection rate effectively quantifies the trade-off between safety and practicality, revealing the LLM's ability to reject objectionable requests while complying with benign requests. Quantitative results on the exploit generation task indicate that, despite performance improvements due to improved coding ability, further research is needed before LLMs can autonomously exploit systems.
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Asjad is an intern consultant at Marktechpost. He is pursuing a degree in mechanical engineering from the Indian Institute of Technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast and is constantly researching applications of machine learning in healthcare.
