Rewriting the Response Path: Silent Tampering and Provider-Signed Defense in BYOK LLM Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions. Developers often trust evidence such as test results and execution logs. We identify a response path integrity gap in Bring Your Own Key configurations used by roughly 88 percent of mainstream agents. Because traffic passes through a user-authorized relay, the relay can modify plaintext LLM responses after alignment but before execution without breaking encryption. A minimal attack rewrites one execution bearing field and regenerates the remaining response using the user key while preserving the model style. Experiments reveal false green verification, where malicious code modifications pass public tests while silently defeating security checks. On APPS, 99.7 percent of publicly passing solutions retained downgraded behavior without developer-visible warnings. Tests on SWE bench, AgentDojo, and ASB across five frontier models show that single-field rewriting can redirect agents while preserving apparent task completion. We propose sign-c, a server-side scheme that authenticates execution bearing fields and outgoing queries. A local shim verifies them before action, while encryption protects confidentiality. The defense rejected all tampered responses with zero false rejections and only 0.0167 percent latency overhead.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 38 of its 69 references that are in the map. First posted on arXiv 4 May 2026.
Builds on
38 of 69 references.
Closest ancestors · 7
Same direction, newest first
- 2026Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain9 Apr
- 2026CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents14 Jan
- 2025AgentBound: Securing Execution Boundaries of AI Agents24 Oct
- 2025Exploiting Web Search Tools of AI Agents for Data Exfiltration10 Oct
- 2025Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agents17 Mar
- 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct
- 2024IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems8 Mar
Other references · 31
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
- 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,326
- 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,124
- 2021Measuring Coding Challenge Competence With APPScited by 879
- 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 791
- 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 633
19 more
- 2026GLM-5: from Vibe Coding to Agentic Engineeringcited by 502
- 2024WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMscited by 460
- 2023Detecting Language Model Attacks with Perplexitycited by 411
- 2024Hybrid LLM: Cost-Efficient and Quality-Aware Query Routingcited by 354
- 2023NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Railscited by 347
- 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 251
- 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 224
- 2022Do Users Write More Insecure Code with AI Assistants?cited by 186
- 2024Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attackscited by 78
- 2022Taxonomy of Attacks on Open-Source Software Supply Chainscited by 57
- 2024SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Mannercited by 57
- 2024Model Equality Testing: Which Model Is This API Serving?cited by 44
- 2024R2-Guard: Robust Reasoning Enabled LLM Guardrail via Knowledge-Enhanced Logical Reasoningcited by 41
- 2025A Closer Look at System Prompt Robustnesscited by 31
- 2024Large Language Model Supply Chain: A Research Agendacited by 23
- 2025Rethinking the Evaluation of Secure Code Generationcited by 10
- 2021Reproducible Builds: Increasing the Integrity of Software Supply Chainscited by 5
- 2026SEAR: Schema-Based Evaluation and Routing for LLM Gatewayscited by 1
- 2026False Security Confidence in Benign LLM Code Generationcited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
- 2026SINGED: Correct Outputs Do Not Certify Safe Execution in LLM Agents
- 2026Securing LLM Agents Need Intent-to-Execution Integrity
- 2026Trust propagation and structural containment in Multi-agent LLM pipelines
- 2026TraceGrant: A Contract-Governed Security Framework for the Task-Effect Lifecycle of Networked LLM Agents
- 2026Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks
- 2026From Approval to Execution: Reconstruction-Aware Repair Analysis for LLM-Agent Software
- 2026AgentSecBench: Measuring Prompt Injection, Privacy Leakage, and Tool-Use Integrity in LLM Agents