GuardianAgentBench: Where Agents Fail and How to Guard Them

• URL: http://arxiv.org/abs/2607.20982v1
• Date: Thu, 23 Jul 2026 07:05:05 GMT
• Title: GuardianAgentBench: Where Agents Fail and How to Guard Them
• Abstract summary: We present GuardianAgentBench, a benchmark of 580 scenarios across six domains evaluated on three production-ready frameworks.<n>We show that even the strongest configuration achieves only 74.8% overall accuracy and expose two distinct failure regimes.<n>Our guardrail implementation consistently outperforms system-prompt-based defenses across all models, recovering 19.9% of failures at a false positive rate of just 0.5%.
• Score: 1.5223648595073074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language model agents increasingly operate autonomously with access to tools and external environments, ensuring their safe and reliable behavior becomes critical. We present GuardianAgentBench (GABench), a benchmark of 580 scenarios across six domains evaluated on three production-ready frameworks: LangChain, LlamaIndex, and Vectara. The benchmark incorporates rigorous multi-stage validation and five adversarial attack modes. Experiments with six state-of-the-art models reveal that even the strongest configuration achieves only 74.8% overall accuracy and expose two distinct failure regimes: stronger models under-call required tools, while weaker models mis-select and over-call tools. Performance degrades monotonically with both tool-set size and sequential turn depth, with long-horizon planning proving the steeper bottleneck. Our guardrail implementation consistently outperforms system-prompt-based defenses across all models, recovering 19.9% of failures at a false positive rate of just 0.5%. These results demonstrate that execution-time structural intervention improves safety without disrupting correct agent behavior.

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