Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
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Abstract (arXiv)
The key limitation of current state-of-the-art influence-based guardrails is that they do not reliably distinguish a legitimate, user-authorized action from a malicious, unauthorized action when both rely on external tool information. This ambiguity can cause benign actions to trigger unnecessary verification and intervention, reducing utility and adding latency. We expose this limitation through an authorization-equivalence audit of 96 conditions derived from 24 base cases. Within matched source comparisons, we hold authorization, the exact committed action, and its intended effect fixed, changing only whether a required value comes from the user or a legitimate tool result. Although the action remains unchanged, this harmless relocation shifts the causal signal toward the attack region in all 24 cases under both Llama and Gemma scorers. Matched unauthorized controls show that the signal remains attack-sensitive, yet the benign relocation produces a larger average score shift than the actual change in authorization. Architecture-level evaluation shows how this mismatch propagates through guardrail designs. With a semantic monitor, attack success is 0% and utility is 28%, compared with 16% and 60% without it. A shadow-based guardrail allows every tested harmless run, yet does not reject matched unauthorized actions more often overall: 57.5% of unauthorized runs pass automatically before reaching the later security check, compared with 29.2% of authorized runs. These results show that the studied causal signal reveals what shaped an action without reliably encoding whether the action was authorized, and that reference construction and routing are integral to the effective security decision.
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 30 of its 34 references that are in the map. First posted on arXiv 30 Aug 2026.
Builds on
30 of 34 references.
Closest ancestors · 16
Same direction, newest first
- 2026FAVA: Formal Authorization for Verified Agents with Evidence-Backed Permission Graphs29 Jul
- 2026Auditing Provenance Sensitivity in LLM Agent Action Selection23 Jul
- 2026Temporary Authority, Permanent Effects: Commit-Time Authorization for LLM Agents11 Jul
- 2026Context-to-Execution Integrity for LLM Agents7 Jul
- 2026Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks27 Jun
- 2026AIRGuard: Guarding Agent Actions with Runtime Authority Control27 May
- 2026Aligning Provenance with Authorization: A Dual-Graph Defense for LLM Agents26 May
- 2026The Granularity Mismatch in Agent Security: Argument-Level Provenance Solves Enforcement and Isolates the LLM Reasoning Bottleneck11 May
- 2026ARGUS: Defending LLM Agents Against Context-Aware Prompt Injection5 May
- 2026Agent-Sentry: Bounding LLM Agents via Execution Provenance24 Mar
- 2026AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations11 Mar
- 2026AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification26 Feb
4 more
- 2026MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks9 Feb
- 2026CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution8 Feb
- 2025Defeating Prompt Injections by Design24 Mar
- 2025MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents7 Feb
Other references · 14
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 441
- 2023Formalizing and Benchmarking Prompt Injection Attacks and Defensescited by 328
- 2019Feature relevance quantification in explainable AI: A causal problemcited by 164
- 2020Aligning Faithful Interpretations with their Social Attributioncited by 79
- 2026ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspacescited by 31
- 2025EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM Systemcited by 26
- 2026Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failurescited by 18
- 2026Overcoming the Retrieval Barrier: Indirect Prompt Injection in the Wild for LLM Systemscited by 16
- 2026Beyond Accuracy: Policy Invariance as a Reliability Test for LLM Safety Judgescited by 8
- 2026The Replay Gap: Static Evaluation of Model Switching in LLM Agents Scores the Wrong Worldcited by 7
- 2025When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Pluginscited by 6
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- 2026The Gate Is Only as Honest as Its Contracts: ContractGuard for the Contract Layer of Risk-Aware Causal Gating
- 2026ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback
- 2026The Claws in Plain Sight: Unauthorized Context Disclosure through LLM Agent Tool Calls
- 2025MindGuard: Intrinsic Decision Inspection for Securing LLM Agents Against Metadata Poisoning
- 2026Representation Transitions Reveal Emerging Safety Risks in Multi-Turn LLM Agents
- 2026Think Twice Before You Act: Protecting LLM Agents Against Tool Description Poisoning via Isolated Planning
- 2026Silent Failures in Agentic Security Evaluation: A Validated Harness for Tool-Call Mediation Under Indirect Prompt Injection