title: Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models
summary: This paper presents CyberSecEval, a comprehensive benchmark developed to help
  bolster the cybersecurity of Large Language Models (LLMs) employed as coding
  assistants. As what we believe to be the most extensive unified cybersecurity
  safety benchmark to date, CyberSecEval provides a thorough evaluation of LLMs
  in two crucial security domains: their propensity to generate insecure code and
  their level of compliance when asked to assist in cyberattacks. Through a case
  study involving seven models from the Llama 2, Code Llama, and OpenAI GPT large
  language model families, CyberSecEval effectively pinpointed key cybersecurity
  risks. More importantly, it offered practical insights for refining these
  models. A significant observation from the study was the tendency of more
  advanced models to suggest insecure code, highlighting the critical need for
  integrating security considerations in the development of sophisticated LLMs.
  CyberSecEval, with its automated test case generation and evaluation pipeline
  covers a broad scope and equips LLM designers and researchers with a tool to
  broadly measure and enhance the cybersecurity safety properties of LLMs,
  contributing to the development of more secure AI systems.
paper_authors._id: 6576739cb238c76bba21eadb
paper_authors.name: Manish Bhatt
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paper_authors.name: Sahana Chennabasappa
paper_authors._id: 6576739cb238c76bba21eadd
paper_authors.name: Cyrus Nikolaidis
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paper_authors.fullname: Cyrus Nikolaidis
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paper_authors.name: Shengye Wan
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paper_authors.name: Ivan Evtimov
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paper_authors.name: Dominik Gabi
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paper_authors.fullname: Dominik Gabi
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paper_authors.user: dkgi
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paper_authors.name: Daniel Song
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paper_authors.name: Faizan Ahmad
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paper_authors.name: Cornelius Aschermann
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paper_authors.name: Lorenzo Fontana
paper_authors._id: 6576739cb238c76bba21eae5
paper_authors.name: Sasha Frolov
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paper_authors.name: Ravi Prakash Giri
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paper_authors.name: Dhaval Kapil
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paper_authors.name: Yiannis Kozyrakis
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paper_authors.name: David LeBlanc
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paper_authors.name: James Milazzo
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paper_authors.name: Aleksandar Straumann
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paper_authors.name: Gabriel Synnaeve
paper_authors.status: admin_assigned
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paper_authors._id: 630eac7931970d1cd4fbacf2
paper_authors.avatarUrl: /avatars/b7ccbddfa745db854dc342be1327cd53.svg
paper_authors.fullname: Gabriel Synnaeve
paper_authors.type: user
paper_authors.user: gsynnaeve
paper_authors._id: 6576739cb238c76bba21eaed
paper_authors.name: Varun Vontimitta
paper_authors.status: admin_assigned
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paper_authors.fullname: Varun Vontimitta
paper_authors.type: user
paper_authors.user: vontimitta
paper_authors._id: 6576739cb238c76bba21eaee
paper_authors.name: Spencer Whitman
paper_authors.status: admin_assigned
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paper_authors.fullname: Spencer Whitman
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paper_authors.user: spencerwmeta
paper_authors._id: 6576739cb238c76bba21eaef
paper_authors.name: Joshua Saxe
paper_authors.status: admin_assigned
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paper_authors.fullname: Joshua Saxe
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paper_title: Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models
paper_summary: This paper presents CyberSecEval, a comprehensive benchmark developed to help
  bolster the cybersecurity of Large Language Models (LLMs) employed as coding
  assistants. As what we believe to be the most extensive unified cybersecurity
  safety benchmark to date, CyberSecEval provides a thorough evaluation of LLMs
  in two crucial security domains: their propensity to generate insecure code and
  their level of compliance when asked to assist in cyberattacks. Through a case
  study involving seven models from the Llama 2, Code Llama, and OpenAI GPT large
  language model families, CyberSecEval effectively pinpointed key cybersecurity
  risks. More importantly, it offered practical insights for refining these
  models. A significant observation from the study was the tendency of more
  advanced models to suggest insecure code, highlighting the critical need for
  integrating security considerations in the development of sophisticated LLMs.
  CyberSecEval, with its automated test case generation and evaluation pipeline
  covers a broad scope and equips LLM designers and researchers with a tool to
  broadly measure and enhance the cybersecurity safety properties of LLMs,
  contributing to the development of more secure AI systems.
paper_ai_summary: CyberSecEval is a benchmark that evaluates the cybersecurity of large language models, focusing on insecure code generation and compliance with cyberattacks, providing insights for enhancing model security.
paper_ai_keywords: Large Language Models LLMs CyberSecEval insecure code cybersecurity cyberattacks