Hidden in Plain Sight: Benchmarking Agent Safety Against Decomposition Attacks with DECOMPBENCH
arXiv:2606.13994v1 Announce Type: cross Abstract: LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world. A key emerging threat is Decomposition Attacks \cite{glukhov2024breach, jones2024adversaries} in which a harmful task is broken into simpler, benign subtasks that evade safety mechanisms when executed separately but cumulatively fulfill the malicious intent. Although recent benchmarks assess agent safety in multi-turn and multi-tool-use settings, they do not explicitly capture this form of decompositional
The rapid advancement and deployment of LLM-based agents necessitate immediate attention to sophisticated adversarial techniques like decomposition attacks, which bypass existing safety measures.
This research highlights a critical vulnerability in current AI safety benchmarks, indicating that widely deployed agents are susceptible to malicious exploitation if not re-evaluated.
The focus on agent safety must expand beyond multi-turn and multi-tool-use scenarios to explicitly address the threat of tasks being broken into benign subtasks to achieve harmful objectives.
- · AI safety researchers
- · Cybersecurity firms specializing in AI
- · Developers of robust AI defense mechanisms
- · Organizations deploying agents without advanced safety protocols
- · Generic AI safety benchmarks
- · Developers neglecting adversarial testing
Immediate industry-wide scramble to update agent safety protocols and develop new defensive mechanisms against decomposition attacks.
Increased demand for specialized AI security audits and a new standard for adversarial robustness in agent deployment.
The emergence of 'AI red teams' as a critical component of every major AI development cycle, focusing on sophisticated attack vectors.
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Read at arXiv cs.AI