Cold-start safety gap found in tool-calling LLM agents
Researchers Chung-En Sun, Linbo Liu, and Tsui-Wei Weng discover that tool-calling LLM agents are most vulnerable to safety threats at the very start of a session, with safety improving by 9–52% after completing a few regular agentic tasks first.
Score breakdown
The discovery that LLM agent safety varies significantly based on conversational position — with a measurable 9–52% improvement after warm-up tasks — identifies a concrete, previously unnamed vulnerability in deployed agentic systems and proposes a benchmark and mitigation strategy grounded in empirical evidence.
- 01Agents are most vulnerable to safety threats at the very start of a session — a phenomenon the authors term the 'cold-start safety gap'.
- 02The authors introduce SODA (Safety Over Depth for Agents), a benchmark supporting up to 20 preceding regular agentic tasks before a safety threat.
- 03Safety improved by 9–52% across 7 models from 4 families as preceding tasks increased from zero to twenty.
Chung-En Sun, Linbo Liu, and Tsui-Wei Weng present a systematic study of a previously uncharacterized vulnerability in tool-calling LLM agents: the cold-start safety gap. Their core finding is that agents are most unsafe at the very start of a conversation session and grow substantially safer as they complete more regular agentic tasks beforehand. To quantify this effect, the authors introduce SODA (Safety Over Depth for Agents), a benchmark designed to control the number of regular agentic tasks completed before an agent encounters a safety-critical request, with support for up to 20 preceding tasks. Across 7 models from 4 model families, safety scores improved by 9–52% as the number of preceding tasks rose from zero to twenty.
Based on these results, the authors recommend a simple deployment strategy: having agents complete a few regular agentic tasks before any possible exposure to safety-critical requests.
The paper's representation analysis provides a mechanistic explanation: model hidden states gradually shift toward a safety-aligned region as more preceding tasks accumulate in context. A further breakdown reveals that the regular agentic tasks themselves are the primary driver of improved safety, while the agent's own prior responses contribute less to safety but are essential for preserving utility in later turns. These findings were validated on open-source safety benchmarks (AgentHarm and Agent Safety Bench) and utility benchmarks (BFCL and API-Bank), confirming that warming up an agent with regular tasks before deployment makes it safer without degrading capability. Based on these results, the authors recommend a simple deployment strategy: having agents complete a few regular agentic tasks before any possible exposure to safety-critical requests. Code is available at https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap.
Key facts
- 01Agents are most vulnerable to safety threats at the very start of a session — a phenomenon the authors term the 'cold-start safety gap'.
- 02The authors introduce SODA (Safety Over Depth for Agents), a benchmark supporting up to 20 preceding regular agentic tasks before a safety threat.
- 03Safety improved by 9–52% across 7 models from 4 families as preceding tasks increased from zero to twenty.
- 04Representation analysis shows model hidden states gradually shift toward a safety-aligned region as more preceding tasks are present.
- 05Regular agentic tasks are the primary driver of safety improvement; the agent's own prior responses matter less for safety but are essential for preserving utility.
- 06Findings were validated on AgentHarm, Agent Safety Bench (safety) and BFCL, API-Bank (utility) benchmarks.
- 07The authors recommend warming up agents with regular agentic tasks before deployment as a mitigation strategy.
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