Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
- URL: http://arxiv.org/abs/2608.29942v1
- Date: Sun, 30 Aug 2026 18:16:04 GMT
- Title: Influence Is Not Authority: When Causal Guardrail Signals Make Legitimate Tool Use Look Like an Attack in Tool-Using LLM Agents
- Abstract summary: Key limitation of influence-based guardrails is that they do not reliably distinguish a legitimate, user-authorized action from a malicious, unauthorized action.<n>We expose this limitation through an authorization-equivalence audit of 96 conditions derived from 24 base cases.<n>A shadow-based guardrail allows every tested harmless run, yet does not reject matched unauthorized actions more often overall.
- Score: 1.0262304700896199
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Recognition Without Enforcement: Configuration-Dependent Failures in LLM Agent Instruction Arbitration and External Control [0.0]
LLM agents arbitrate among instructions from system prompts, users, memory, and tools, but this arbitration cannot be assumed to enforce trust boundaries.<n>We identify a recognition-enforcement gap: source-format features are linearly decodable from model activations, and models can explicitly identify forged authority when prompted.<n>We treat model self-arbitration as a capability rather than a security boundary and implement an external reference monitor combining authenticated source routing with capability-gated tool execution.
arXiv Detail & Related papers (2026-08-28T16:34:05Z) - ROPE: Routed Origin Policy Enforcement against Indirect Prompt Injection [61.4148196085256]
We present ROPE (Routed Origin Policy Enforcement), which is anchored in a structural notion of trust.<n>Our approach admits two provable guarantees: 1) at every step of a trajectory, no value whose only origin is attacker-writable content reaches an origin-guarded parameter, and 2) no rewording of an injection changes an admission decision.
arXiv Detail & Related papers (2026-08-27T01:22:14Z) - Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents [0.0]
Agentic red-teaming benchmarks report whether an injected agent was compromised as a single bit: the attack succeeded, or it did not.<n>We argue that this binary attack-success rate discards the information a defender most needs, namely how harmful the resulting action was.<n>We introduce an action-graded harm rubric that scores an agent's tool-call trajectory on a seven-level ordinal scale (L0 to L6) according to whether the executed action was reversible, whether it crossed scope to reach another party, and whether it expanded privilege.
arXiv Detail & Related papers (2026-07-08T14:38:01Z) - Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority [7.392721604463545]
We show that confinement can be guaranteed as an invariant of the agent's execution architecture.<n>We prove that no amount of learning, skill acquisition, or self-induced governance abstraction can widen the agent's permitted authority.
arXiv Detail & Related papers (2026-07-06T02:42:06Z) - Trust No Tool: Evaluating and Defending LLM Agents under Untrusted Tool Feedback [38.251599309409]
We study a different failure mode, cognitive poisoning, in which a malicious tool behaves plausibly during exploration.<n>We present VISTA-Guard, a backbone-agnostic framework for final-action risk scoring.
arXiv Detail & Related papers (2026-05-17T13:51:34Z) - CIVeX: Causal Intervention Verification for Language Agents [0.0]
We introduce CIVeX, a causal intervention verifier that maps proposed actions to structural causal queries over a committed action-state graph.<n>On Causal-ToolBench (1,890 instances, 7 seeds), CIVeX yields zero observed false executions across moderate and adversarial confounding.<n>On IHDP and ZOZO Open Bandit (real production logs with uniform-random ground truth), CIVeX matches Oracle correct-execution within 0.1pp and cuts per-execute false-execution by >=50x over naive baselines.
arXiv Detail & Related papers (2026-05-09T21:06:15Z) - Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors [48.881343993730844]
Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines.<n> adversaries can distribute backdoored checkpoints that behave normally under standard evaluation but jailbreak when a hidden trigger is present.<n>Recent post-hoc weight-editing methods offer an efficient approach to injecting such backdoors.
arXiv Detail & Related papers (2026-04-14T06:48:33Z) - When Actions Go Off-Task: Detecting and Correcting Misaligned Actions in Computer-Use Agents [50.5814495434565]
This work makes the first effort to define and study misaligned action detection in computer-use agents (CUAs)<n>We identify three common categories in real-world CUA deployment and construct MisActBench, a benchmark of realistic trajectories with human-annotated, action-level alignment labels.<n>We propose DeAction, a practical and universal guardrail that detects misaligned actions before execution and iteratively corrects them through structured feedback.
arXiv Detail & Related papers (2026-02-09T18:41:15Z) - CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution [49.689452243966315]
AI agents equipped with tool-calling capabilities are susceptible to Indirect Prompt Injection (IPI) attacks.<n>We propose CausalArmor, a selective defense framework that computes lightweight, leave-one-out attributions at privileged decision points.<n> Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses.
arXiv Detail & Related papers (2026-02-08T11:34:08Z) - A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness [57.510025257780306]
We show that existing validation protocols fail to account for substantial distribution shifts inherent to red-teaming.<n>We propose ReliableBench, a benchmark of behaviors that remain more consistently judgeable, and JudgeStressTest, a dataset designed to expose judge failures.
arXiv Detail & Related papers (2026-02-04T15:13:35Z) - Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
arXiv Detail & Related papers (2026-01-23T17:17:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.