SafeClawBench: Separating Semantic, Audit-Evidence, and Sandbox Harm in Tool-Using LLM Agents
arXiv:2606.18356v1 Announce Type: cross Abstract: Tool-using language-model agents introduce security failures that go beyond unsafe text: they can disclose protected objects, write persistent memory, send messages, modify databases, or trigger harmful code and tool effects. Existing evaluations often collapse these stages into a single attack success rate, making it difficult to tell whether a model merely agreed with an attacker or actually produced observable harm. We introduce SafeClawBench, a staged benchmark for tool-using agent security with 600 controlled adversarial tasks across six a
The proliferation of tool-using LLM agents necessitates robust security benchmarks as these systems move from research to deployment, revealing inherent risks now rather than later.
This benchmark directly addresses critical security vulnerabilities in AI agents, impacting their safe and reliable integration across industries and potentially influencing regulatory frameworks.
The ability to accurately differentiate between semantic agreement and actual harmful outcomes in AI agent security evaluation will lead to more targeted and effective mitigation strategies.
- · AI security researchers
- · Enterprises deploying AI agents
- · Cybersecurity firms
- · Malicious actors
- · AI developers ignoring security by design
Improved security protocols and evaluations for tool-using LLM agents become standard practice.
Increased user and enterprise trust in the deployment and capabilities of AI agent systems.
Accelerated adoption of AI agents in sensitive applications, reshaping white-collar workflows with greater integrity.
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Read at arXiv cs.AI