Top five strategies from Meta’s CyberSecEval 3 to combat weaponized LLMs
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With weaponized large language models (LLMs) becoming lethal, stealthy by design and challenging to stop, Meta has created CyberSecEval 3, a new suite of security benchmarks for LLMs designed to benchmark AI models’ cybersecurity risks and capabilities.
“CyberSecEval 3 assesses eight different risks across two broad categories: risk to third parties and risk to application developers and end users. Compared to previous work, we add new areas focused on offensive security capabilities: automated social engineering, scaling manual offensive cyber operations, and autonomous offensive cyber operations,” write Meta researchers.
Meta’s CyberSecEval 3 team tested Llama 3 across core cybersecurity risks to highlight vulnerabilities, including automated phishing and offensive operations. All non-manual elements and guardrails, including CodeShield and LlamaGuard 3 mentioned in the report are publicly available for transparency and community input. The following figure analyzes the detailed risks, approaches and results summary.
