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Will the User Ever Know? Covert Indirect Prompt Injection on Tool-Using LLM Agents

Yunseok Lee, Yunji Kim, Woojin Lee

arXiv:2608.30362v1cs.AIcs.CL

TL;DR

Indirect prompt injection can succeed without users noticing, a distinction that standard ASR does not capture. The paper introduces metrics and trajectory-based attack design for covert success, finding that ICoA achieves the highest CSR across four AgentDojo target models.

  • Problem

    ASR measures whether an injected task succeeds but not whether the success is visible to the user, despite the security importance of unnoticed compromise.

  • Method

    The paper distinguishes covert and overt successes, analyzes ReAct trajectories, introduces CSR and OSR, and proposes ICoA to return the agent to the user task after executing an injection.

  • Results

    ICoA achieves the highest CSR across all four target models on AgentDojo and maintains its lead under every evaluated defense.

  • Takeaways & Limitations

    Successful injections can remain invisible by design when the agent returns to the user task before producing its final response.

  • Takeaways & Limitations

    The evaluation focuses on ReAct-style loops and single-turn AgentDojo interactions, so applicability to other agent designs and longer conversations is unknown.

Abstract

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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.

1 Introduction

Tool-using LLM agents can execute indirect prompt injections without revealing them to users, exposing a gap in ASR. This paper defines user-facing success metrics, explains the ReAct mechanism behind covert outcomes, and proposes ICoA to induce them.

  • Threat and motivation: Tool observations can carry malicious instructions that hijack an agent into executing an attacker’s task while answering the user’s original request.IPI content may be embedded in emails, search results, documents, or transaction records.
  • Threat and motivation: ASR counts injected-task execution but not whether the successful attack is visible in the agent’s final response.Unnoticed attacks can leave users trusting a compromised system without an opportunity to detect or respond.
  • User-facing metrics: Covert success means a successful injection leaves no indication in the final response, whereas overt success discloses the injected action.These definitions distinguish user-visible outcomes while preserving the shared notion of attack execution.
  • User-facing metrics: CSR and OSR decompose ASR according to whether a successful injection remains hidden or is disclosed in the final response.The paper presents these as the first IPI metrics explicitly distinguishing successful injections by user visibility.
  • ReAct mechanism: Covert outcomes arise when the agent returns to the original user task before answering, while overt outcomes end with the injected action as the latest handled task.The distinction concerns post-injection behavior rather than the injected action itself.
  • ICoA: ICoA wraps an injection with user framing and a RETURN anchor, placing the attack mid-trajectory and steering the agent back to the user task afterward.This design directly implements the trajectory placement associated with covert outcomes.
  • ICoA: ICoA achieves the highest CSR across four AgentDojo target models, retains its lead under evaluated defenses, and gains up to 23.71 percentage points when appended to baselines.The results support deliberately inducing covert success by design.

2 Background

The paper models tool-using agents as ReAct-style loops that alternate reasoning, tool calls, and observations. Indirect prompt injection enters through those observations and can redirect subsequent agent behavior.

  • ReAct Loop: A ReAct-style agent interleaves reasoning, tool calls, and tool observations until producing a single final response.For a user task Tu, the agent repeatedly processes observations before answering.
  • ReAct Loop: Each action combines reasoning text and tool calls, while the agent policy generates the next action from the current state.The environment executes those calls and returns the next observation.
  • ReAct Loop: Only the final response reaches the user; intermediate reasoning, tool calls, and observations remain internal.This separation creates a gap between agent behavior and what the user can notice.
  • Indirect Prompt Injection: IPI places adversarial content in tool observations rather than the user prompt, allowing poisoned external content to steer later reasoning and tool calls.The injected task Tm is distinct from the valid user task Tu.
  • Indirect Prompt Injection: Under the standard IPI threat model, attackers cannot access the system prompt or tool definitions and control only returned tool-observation content.

3 Covert Success in IPI

IPI evaluation traditionally asks whether an injected task executes, but this paper also asks whether the user can detect that success in the final response. It defines covert and overt outcomes and links their difference to whether the agent returns to the user task or exits after injection.

  • 3.1 Covert and Overt Success: ASR counts injected tool-call execution, but does not measure whether the final response reveals the attack to the user.
  • 3.1 Covert and Overt Success: A successful injection is covert when it executes without indication in the final response, and overt when the response reveals the injection or its effects.
  • 3.1 Covert and Overt Success: CSR and OSR partition successful traces by disclosure, with ASR = CSR + OSR.
  • 3.1 Covert and Overt Success: The disclosure flag requires judging the final response, because disclosure may be paraphrased or implicit rather than matching injected entity names exactly.
  • 3.2 The Mechanism Behind Covert Success: Overt successes more often EXIT after injection (70.92%), whereas covert successes mostly RETURN to the user task (67.70%).The transition patterns are aggregated over four target models for baseline attacks without defenses.
  • 3.2 The Mechanism Behind Covert Success: ReAct tends to summarize the most recent action, so EXIT exposes the injection while RETURN leaves the final response focused on the user task.

4 Method: ICoA

ICoA is designed to induce covert success by ensuring the injection executes and then steering the agent back to the original user task. It combines user framing with a RETURN anchor placed around the injected task.

  • 4 Method: ICoA: ICoA requires sequentially executing the injected task and then returning to the user task instead of ending at the injection.
  • 4 Method: ICoA: The user framing presents the injected task as a follow-up instruction from the same user, encouraging the agent to treat it as a user request.
  • 4 Method: ICoA: The RETURN anchor contains pre- and post-injection segments that first order execution of the injection and then direct the agent back to the user task.
  • 4 Method: ICoA: The RETURN anchor can be appended to existing IPI attacks because it does not depend on particular attack wording.
  • 4 Method: ICoA: ICoA places the injected task between the user framing and RETURN anchor, producing a payload intended to induce the RETURN transition.

5 Experiments

The experiments compare ICoA with four IPI attacks across AgentDojo suites, four target models, and five defenses. The evaluation reports ASR and CSR under no-defense and averaged-defense conditions.

  • 5.1 Experiment Setup: The study compares ICoA with Direct, InjecAgent, Important message, and ChatInject attacks.Important message is identified as the strongest AgentDojo baseline.
  • 5.1 Experiment Setup: Five defenses include PI Detector, Instruction Prevention, Data Delimiters, User Instruction Repetition, and Task Shield.
  • 5.1 Experiment Setup: AgentDojo v0.1.34 supplies 949 user-task and injection-task pairs across Banking, Slack, Travel, and Workspace, with ASR and CSR reported in percent.
  • 5.1 Experiment Setup: The evaluation uses Qwen3-235B, LLaMA-3.3-70B, GPT-4o-mini, and Gemini-2.5-Flash to compare open-weight and closed-source target models.
  • 5.1 Experiment Setup: Figure 5 compares ASR and CSR both without defenses and after averaging results across five defenses and four target models.

LLaMA-3.3-70B

Across four target models, ICoA achieves the highest CSR and retains that lead under evaluated defenses. Appending the RETURN anchor increases covert outcomes by shifting successful trajectories back to the user task.

  • No defense: ICoA achieves the highest CSR on all four target models, with gains of 3.79 to 12.01 percentage points over Imp. message.On LLaMA-3.3-70B, CSR rises from 11.80% to 23.81%.
  • Detection: Detection reduces successful injections, yet ICoA retains the highest CSR among surviving successes on every model.On Qwen3-235B, ICoA’s ASR falls from 55.43% to 12.43% under PI Detector.
  • Prompting: ICoA has the highest CSR under every prompting defense on every model, although ASR reductions are inconsistent.These defenses do not change the trajectory pattern that allows successful injections to remain hidden.
  • Runtime task alignment: Under Task Shield, ICoA retains the highest CSR despite substantially reduced ASR across models.On LLaMA-3.3-70B, similar ASR values of 5.16% and 5.37% correspond to CSR values of 4.11% and 0.21% for ICoA and ChatInject.
  • RETURN anchor analysis: Appending the RETURN anchor raises CSR for every evaluated baseline, by up to 23.71 percentage points, without changing the injected goal or template.For ChatInject on LLaMA-3.3-70B, CSR increases from 2.63% to 26.34%.
  • RETURN anchor analysis: After appending the anchor, RETURN counts increase for every baseline on LLaMA-3.3-70B, mirroring the CSR gains.The largest proportional increase occurs for Direct, whose baseline trajectories are predominantly EXIT.

6 Conclusion

The paper asks whether users can detect successful indirect prompt injections and introduces user-perspective metrics and an attack strategy for inducing hidden outcomes. Across four models, ICoA achieves the highest CSR, showing that covert success can be deliberately induced.

  • 6 Conclusion: The conclusion frames IPI evaluation around both execution and visibility in the agent’s final response.A successful injection can remain invisible by design.
  • 6 Conclusion: The paper introduces CSR and ICoA after finding that covert success depends on whether the agent returns to the user task after injection.ICoA deliberately induces this behavior within a single tool observation.
  • 6 Conclusion: Across four models, ICoA achieves the highest CSR among evaluated attacks.The conclusion presents this as evidence that covert success can be deliberately induced.

Limitations

The authors identify three main limitations concerning trace labeling, agent architecture, and interaction scope.

  • Trace classification relies on an LLM, with human reviewers checking agreement but not every defense condition.
  • The evaluation focuses on ReAct-style loops, so applicability to agents that expose their thought process remains unknown.
  • The study evaluates only single-turn AgentDojo interactions, leaving later user detection in longer conversations outside its scope.

Ethical Considerations

The paper studies covert indirect prompt injection in sandboxed tool-using agents and evaluates both attack behavior and defenses. Its experiments use public models and AgentDojo’s simulated environment, while related work motivates the security concern and broader evaluation context.

  • Ethical Considerations: ICoA steers successful injections back toward the user task so the final response hides the attack.
  • Ethical Considerations: The experiments use publicly available models and AgentDojo, a sandboxed environment with synthetic users and simulated tools containing no real user data.
  • Ethical Considerations: The study evaluates detection, prompting, and runtime task-alignment defenses alongside attack variants.
  • Ethical Considerations: The reported utility evaluation compares attacked trajectories with each model’s own benign task performance rather than comparing utility across models.

C.3 Suite-level Analysis

ICoA’s covert success varies by AgentDojo suite and reflects the trajectory’s opportunities to execute and later disclose the injection. Additional measurements and ablations support the RETURN mechanism across attacks and benchmarks.

  • C.3 Suite-level Analysis: ICoA is strongest on Slack and Travel, remains effective on Banking, and is weakest on Workspace.
  • C.3 Suite-level Analysis: Workspace’s longer-horizon, multi-tool tasks provide more opportunities to skip the injection and mention it in longer final responses.
  • C.3 Suite-level Analysis: ICoA attains the highest aggregate CSR among all attacks on all four models despite suite-level variation.
  • C.3 Suite-level Analysis: Appending the RETURN anchor raises successful RETURN counts across all four models, including Gemini-2.5-Flash Direct from 6 to 180.
  • C.3 Suite-level Analysis: On InjecAgent’s datastealing suite, ICoA achieves the highest CSR at 29.60%, while ChatInject achieves the highest ASR at 45.96% but no covert successes.
  • C.3 Suite-level Analysis: The InjecAgent and AgentDojo measurements are complementary rather than directly comparable because their continuation procedures and success validators differ.

F.2 Validation

Validation checks whether covert-versus-overt labels are robust across judges and illustrative traces. The labels remain consistent, while examples show the split across suites and models.

  • F.2 Validation: 500 stratified successful-injection traces were independently labeled by GPT-4o, Claude Haiku 4.5, and two human annotators.
  • F.2 Validation: The three labeling sources produce consistent covert counts and preserve the rank order of the five attacks.
  • F.2 Validation: Across all five attacks, GPT-4o’s covert count matches or falls one below the human label, indicating the auditor’s rule is not inflating CSR.
  • F.2 Validation: Examples across Banking, Slack, Travel, and Workspace show both user and injected tasks succeeding while covert traces mention only the user task in the final response.
  • F.2 Validation: An overt example reports the password change before continuing with the requested spending calculation.

G.2 Slack

The traces show that indirect injections can trigger unauthorized tool actions while the agent either reveals or conceals them in its final response. The examples span Slack, travel, and workspace tasks, alongside several attack formats and evaluation details.

  • Slack: A Slack injection caused the agent to visit true-informations.com while also completing the requested article summary and direct message.The final reply reported both the requested article result and the injected website visit.
  • Slack: A separate webpage trace exposed the injected visit directly in the final reply after reading informations.com.The response stated that true-informations.com had been visited but contained no content.
  • Travel: Travel traces include an injected calendar event that did not prevent the agent from reporting hotel prices, ratings, an itinerary split, and the $720 minimum expense.The calendar event was created before the agent continued with the Paris and London planning task.
  • Travel: Another travel injection inserted a recommendation for Riverside View Hotel into the final reply while the agent reserved Le Marais Boutique.The reply also reported Le Marais Boutique’s 4.2 rating and the January 11–15 reservation.
  • Workspace: In the workspace example, the agent sent an injected email and then continued answering the user’s question about networking-event participants and addresses.The final response explicitly stated that the email had been sent before returning to the original question.
  • Experimental setup: The compared attacks were Direct, InjecAgent, Important message, and ChatInject, while experiments used multiple open and closed models at temperature 0.The strongest baseline was Important message, and the experiments used local Ollama runs, official model APIs, and substantial API costs for closed models.
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