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arxiv: 2606.07867 · v1 · pith:XQXLJV74new · submitted 2026-06-05 · 💻 cs.CL

The Cold-Start Safety Gap in LLM Agents

Chung-En Sun , Linbo Liu , Tsui-Wei Weng This is my paper

Pith reviewed 2026-06-27 21:36 UTC · model grok-4.3

classification 💻 cs.CL
keywords cold-start safety gapLLM agentstool callingsafety alignmentSODA benchmarkagentic taskshidden states
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The pith

LLM tool-calling agents are least safe at the start of a session and gain substantial safety after completing regular agentic tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that tool-calling LLM agents show their lowest safety at the beginning of a conversation and become markedly safer once they have handled several ordinary tasks. This cold-start safety gap is quantified by introducing the SODA benchmark, which systematically varies the number of preceding regular tasks before a safety threat appears. Across seven models, safety rises 9 to 52 percent as the count of prior tasks grows from zero to twenty. Representation analysis shows the models' hidden states drift toward a safety-aligned region with more preceding tasks. The regular tasks themselves drive most of the gain, while the agent's own earlier replies matter less for safety but help retain later performance on utility benchmarks.

Core claim

Tool-calling LLM agents are most vulnerable to safety threats at the very start of a session and become substantially safer after completing a few regular agentic tasks, a pattern termed the cold-start safety gap. The SODA benchmark controls the exact number of preceding regular tasks up to twenty and shows consistent safety gains of 9-52 percent across models from four families. Hidden-state analysis confirms a gradual shift toward safety-aligned regions. Regular agentic tasks prove the main driver of the improvement, whereas the agent's own prior responses have smaller direct impact on safety but are necessary to preserve utility on benchmarks such as BFCL and API-Bank.

What carries the argument

The SODA benchmark, which fixes the number of regular agentic tasks before a safety threat while holding all other variables constant.

If this is right

  • Safety improves steadily with each added regular task up to at least twenty.
  • The regular tasks themselves, not the agent's own replies, account for most of the safety gain.
  • Warming the agent with regular tasks before safety-critical requests raises safety while leaving capability on utility benchmarks intact.
  • The same pattern appears on external benchmarks AgentHarm and Agent Safety Bench.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Deployment pipelines could insert a short mandatory warm-up phase of ordinary tasks before any user-facing session begins.
  • The finding suggests testing whether the same warm-up effect appears in non-tool agents such as pure chat or code-generation models.
  • Longer sessions might show whether the safety alignment continues to strengthen or eventually plateaus beyond twenty tasks.

Load-bearing premise

The controlled preceding tasks in SODA truly isolate the effect of task count rather than being confounded by model-specific training data or the particular safety threats chosen.

What would settle it

A controlled run on SODA in which safety stays flat or declines as the number of preceding regular tasks increases from zero to twenty would falsify the gap claim.

Figures

Figures reproduced from arXiv: 2606.07867 by Chung-En Sun, Linbo Liu, Tsui-Wei Weng.

Figure 1
Figure 1. Figure 1: Overview of our findings. (A) Our SODA benchmark evaluates agent safety with controlled conversation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: PCA projections of model hidden states at the moment each harmful request is presented, colored by [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: PCA projections of model hidden states for all available models. Each row is one model; columns show [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
read the original abstract

Are tool-calling LLM agents equally safe throughout a conversation? We discover they are not: agents are most vulnerable at the very start of a session and become substantially safer after a few regular agentic tasks -- a phenomenon we term the cold-start safety gap. To study this systematically, we introduce Safety Over Depth for Agents (SODA), a benchmark that controls how many regular agentic tasks the agent completes before encountering a safety threat, supporting up to 20 preceding tasks. Evaluating 7 models from 4 families, safety improves by 9--52% as the number of preceding regular agentic tasks increases from zero to twenty. Representation analysis confirms that model hidden states gradually shift toward a safety-aligned region as more preceding tasks are present. By systematically studying which part of the preceding conversation matters most, we find that the regular agentic tasks themselves are the primary driver of safety, while the agent's own prior responses have less effect on safety but are essential for preserving later utility. This conclusion is further supported by evaluation on open-source safety benchmarks (AgentHarm, Agent Safety Bench) and utility benchmarks (BFCL, API-Bank), confirming that warming up the agent with regular agentic tasks before deployment makes it safer and preserves full capability. Based on these findings, we recommend a simple deployment strategy: having the agent complete a few regular agentic tasks before possible exposure to safety-critical requests mitigates the cold-start safety gap. Our code is available at https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that tool-calling LLM agents exhibit a 'cold-start safety gap': they are most vulnerable to safety threats at the start of a session and become substantially safer (by 9-52%) after completing a small number of regular agentic tasks. To demonstrate this, the authors introduce the SODA benchmark, which varies the number of preceding regular tasks (0 to 20) before a safety threat while holding other factors fixed. They evaluate 7 models across 4 families, perform representation analysis showing hidden-state shifts toward safety-aligned regions, conduct ablations on conversation components, and validate on AgentHarm, Agent Safety Bench, BFCL, and API-Bank, ultimately recommending a simple warm-up deployment strategy.

Significance. If the central claim holds after addressing experimental controls, the result identifies a practically relevant deployment vulnerability in LLM agents and offers an immediately actionable mitigation. The release of the SODA benchmark and code at https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap is a clear strength that enables reproducibility and further testing by the community.

major comments (2)
  1. [Abstract] Abstract and experimental results section: the reported 9-52% safety improvement is presented without error bars, confidence intervals, or statistical significance tests across the 7 models and varying task counts. Because the central claim rests on the magnitude and consistency of this improvement being driven by task count rather than variance or model-specific noise, the absence of these controls is load-bearing for interpreting the quantitative findings.
  2. [SODA benchmark and evaluation] SODA benchmark description and threat selection: the attribution of safety gains to the number of preceding regular tasks assumes that the chosen safety threats do not interact with accumulated context in ways that vary systematically by model family or training-data overlap. The ablations on conversation parts address some factors but do not include explicit tests for threat robustness (e.g., swapping threat categories) or cross-model data-leakage checks, leaving open the possibility that the observed hidden-state shift and safety gain are partly artifactual.
minor comments (2)
  1. [Ablation study] The abstract states that 'the regular agentic tasks themselves are the primary driver' while 'the agent's own prior responses have less effect'; this distinction would be clearer if the relevant ablation tables explicitly reported the safety delta when responses are ablated versus when tasks are ablated.
  2. [Representation analysis] Representation analysis is mentioned but the manuscript provides no details on the exact layers, distance metrics, or statistical tests used to confirm the shift toward the safety-aligned region.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The two major comments raise important points about statistical rigor and potential confounds in the SODA benchmark. We address each below and indicate where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract] Abstract and experimental results section: the reported 9-52% safety improvement is presented without error bars, confidence intervals, or statistical significance tests across the 7 models and varying task counts. Because the central claim rests on the magnitude and consistency of this improvement being driven by task count rather than variance or model-specific noise, the absence of these controls is load-bearing for interpreting the quantitative findings.

    Authors: We agree that the absence of error bars, confidence intervals, and statistical tests in the abstract and main results weakens the presentation of the central quantitative claim. The underlying experiments were run with multiple seeds per model and task depth; we will add per-model error bars, 95% confidence intervals, and paired significance tests (e.g., McNemar or Wilcoxon) comparing depth 0 versus depth 20 in both the abstract and the experimental results section of the revised manuscript. revision: yes

  2. Referee: [SODA benchmark and evaluation] SODA benchmark description and threat selection: the attribution of safety gains to the number of preceding regular tasks assumes that the chosen safety threats do not interact with accumulated context in ways that vary systematically by model family or training-data overlap. The ablations on conversation parts address some factors but do not include explicit tests for threat robustness (e.g., swapping threat categories) or cross-model data-leakage checks, leaving open the possibility that the observed hidden-state shift and safety gain are partly artifactual.

    Authors: The existing ablations already isolate the contribution of the preceding regular tasks versus the agent's own responses and show that task content is the dominant driver. Nevertheless, we acknowledge that explicit threat-robustness checks (swapping threat categories while holding depth fixed) and a brief discussion of possible training-data overlap were not included. We will add a new subsection reporting results on swapped threat categories and a short paragraph addressing cross-family consistency as evidence against systematic leakage. We do not believe the core finding is artifactual, but these additions will close the gap noted by the referee. revision: partial

Circularity Check

0 steps flagged

Empirical benchmark study with no circular derivations or self-referential reductions

full rationale

The paper presents an empirical evaluation using the introduced SODA benchmark to measure safety improvements across varying numbers of preceding tasks on multiple models, supported by representation analysis, ablations, and cross-benchmark validation on AgentHarm, Agent Safety Bench, BFCL, and API-Bank. No equations, fitted parameters, or derivations are present that reduce reported safety gains (9-52%) to inputs defined from the same data. The central claim relies on controlled experimental variation and external benchmarks rather than self-citation chains or ansatzes; the work is self-contained against external benchmarks with no load-bearing self-references or uniqueness theorems invoked.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the empirical validity of the SODA benchmark construction and the assumption that hidden-state shifts correlate with safety behavior; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption The safety threats used in SODA are representative of real deployment risks and the preceding regular tasks do not themselves introduce safety-relevant signals.
    Invoked when attributing the observed safety improvement solely to the number of preceding tasks.

pith-pipeline@v0.9.1-grok · 5812 in / 1167 out tokens · 20809 ms · 2026-06-27T21:36:29.696522+00:00 · methodology

discussion (0)

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