"Agent-SafetyBench: Evaluating the Safety of LLM Agents"

First comprehensive framework to test if AI agents can safely handle real-world tasks without causing harm.

AgentSafetyBench introduces a comprehensive evaluation framework with 349 environments and 2,000 test cases to assess safety risks in LLM agents across 8 risk categories.

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https://arxiv.org/abs/2412.14470

🔍 Original Problem:

While LLMs increasingly operate as agents using various tools, there's no systematic way to evaluate their safety risks beyond content generation. Current benchmarks lack comprehensive coverage of interactive environments.

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🛠️ Solution in this Paper:

→ AgentSafetyBench evaluates agents across 349 diverse interaction environments from email systems to smart city controls

→ Implements 2,000 test cases covering 8 risk categories from data leaks to physical harm

→ Uses fine-tuned Qwen-2.5-7B-Instruct as automated scorer, achieving 15% better accuracy than GPT-4

→ Evaluates 10 common failure modes in unsafe agent interactions

→ Provides standardized test format with risks, instructions, environments and failure modes

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💡 Key Insights:

→ LLM agents show more significant flaws in behavioral safety compared to content safety

→ Two fundamental safety defects: lack of robustness and lack of risk awareness

→ Defense prompts provide only limited safety improvements

→ Proprietary models outperform open-source ones in safety metrics

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📊 Results:

→ No tested agent achieved safety score above 60%

→ Claude series performed best but still showed significant safety gaps

→ Average safety score across all agents was 38.5%

→ Behavioral safety scores (30.4%) significantly lower than content safety (68.4%)

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