LLM Agents Can Easily Tamper With Their Own Traces
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
Asynchronous monitoring, incident investigations, and compliance audits primarily rely on agent traces to reconstruct what happened. These analyses assume that LLM agents cannot tamper with their own execution traces. We show that local LLM agents such as Claude Code, Codex, Antigravity, Open Code and Grok Build fail to enforce this boundary. All tested harnesses, except Muse Code, allowed agents to delete their traces when asked, without triggering monitor guardrails. We also validate that external attackers can exploit this gap to induce trace deletion. Finally, we show that trace tampering behavior emerges naturally in frontier models, when agents try to improve their rewards. We advise practitioners to ensure trace logging happens through an independent interception mechanism outside of the agent's control, preserving trace integrity even in cases of full host compromise. Overall, our findings identify a concrete failure of trace integrity in agent infrastructure which can be used to conceal misaligned behaviors like scheming or sabotage.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 14 of its 26 references that are in the map. First posted on arXiv 24 Sep 2026.
Builds on
14 of 26 references.
Closest ancestors · 4
Same direction, newest first
- 2026From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents3 Jun
- 2026Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks23 Feb
- 2025VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces17 Dec
- 2025Agentic Misalignment: How LLMs Could Be Insider Threats5 Oct
Other references · 10
- 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscationcited by 384
- 2024Frontier Models are Capable of In-context Schemingcited by 287
- 2024Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Modelscited by 178
- 2022Goal Misgeneralization: Why Correct Specifications Aren't Enough For Correct Goalscited by 126
- 2023Evaluating Language-Model Agents on Realistic Autonomous Taskscited by 96
- 2025Auditing language models for hidden objectivescited by 83
- 2024Feedback Loops With Language Models Drive In-Context Reward Hackingcited by 77
- 2026Stealing Reasoning Traces from Proprietary LLM APIscited by 9
- 2026Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviorscited by 3
- 2026Agent Flight Recorder: Tamper-Evident Audit Trails with On-Chain Anchoring for Long-Horizon Tool-Using Agentscited by 2
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- 2025TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
- 2026Local LLM Agents as Vulnerable Runtimes:A Source-Code Audit of the Agent Runtime Layer
- 2026Towards a Unified Misuse Monitoring Benchmark
- 2026Securing LLM Agents Need Intent-to-Execution Integrity
- 2026ContainmentBench: Trace-Based Evaluation of Post-Exposure Containment in Tool-Using LLM Agents
- 2026Why LLM Agents Collapse Without Oversight: The Enforcement Gap as the Mechanism Behind Emergence World Failures
- 2026Agent Audit: A Security Analysis System for LLM Agent Applications
- 2026Securing LLM-Agent Long-Term Memory Against Poisoning: Non-Malleable, Origin-Bound Authority with Machine-Checked Guarantees