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This research exposes a critical blind spot in LLM agent security by distinguishing between overt attacks visible in final responses and covert injections that execute malicious actions while maintaining normal output. By introducing the Covert Success Rate (CSR) metric, the study highlights how current evaluation standards underestimate risks where agents silently hand control back to user tasks after executing an injection. The proposed ICoA attack demonstrates a significant increase in undetectable success rates across multiple models, underscoring the urgent need for new safety benchmarks that account for post-injection agent behavior.
Will the User Ever Know? Covert Indirect Prompt Injection on Tool-Using LLM Agents
arXiv:2608.30362v1 Announce Type: cross Abstract: As LLM agents take real-world actions through tools, indirect prompt injection (IPI) has emerged as a serious threat. The standard metric, Attack Success Rate (ASR), counts whether an injection succeeds but ignores what the user notices in the agent's final response. Looking at successful injection traces, we find two distinct outcomes: the agent executes the injection while returning an otherwise normal response, or reports the injected action in its final response, giving the user a chance to notice. We call these covert and overt successes. From the user's perspective, we decompose ASR into the Covert Success Rate (CSR), counting successes leaving no trace in the final response, and the Overt Success Rate (OSR), counting successes the user can detect. To understand what drives the gap, we analyze successful trajectories and find that the agent's behavior after the injection separates covert from overt: covert traces hand control back to the user task before ending, while overt traces end at the attack itself. This split follows from the ReAct format, where the final response summarizes the most recent action. Building on this observation, we propose ICoA (Induced Covert Attack), an IPI attack designed to induce covert outcomes by steering the agent back to the user task after executing the injection. Across four target models on AgentDojo, ICoA achieves the highest CSR, with gains of 3.79-12.01 percentage points over the strongest baseline.
Title:Will the User Ever Know? Covert Indirect Prompt Injection on Tool-Using LLM Agents
Abstract:As LLM agents take real-world actions through tools, indirect prompt injection (IPI) has emerged as a serious threat. The standard metric, Attack Success Rate (ASR), counts whether an injection succeeds but ignores what the user notices in the agent's final response. Looking at successful injection traces, we find two distinct outcomes: the agent executes the injection while returning an otherwise normal response, or reports the injected action in its final response, giving the user a chance to notice. We call these covert and overt successes. From the user's perspective, we decompose ASR into the Covert Success Rate (CSR), counting successes leaving no trace in the final response, and the Overt Success Rate (OSR), counting successes the user can detect. To understand what drives the gap, we analyze successful trajectories and find that the agent's behavior after the injection separates covert from overt: covert traces hand control back to the user task before ending, while overt traces end at the attack itself. This split follows from the ReAct format, where the final response summarizes the most recent action. Building on this observation, we propose ICoA (Induced Covert Attack), an IPI attack designed to induce covert outcomes by steering the agent back to the user task after executing the injection. Across four target models on AgentDojo, ICoA achieves the highest CSR, with gains of 3.79-12.01 percentage points over the strongest baseline.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2608.30362 [cs.AI]
(or arXiv:2608.30362v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.30362 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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