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How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition
Mateusz Dziemian, Maxwell Lin, Xiaohan Fu, Micha Nowak, Nick Winter, Eliot Jones, Andy Zou, Lama Ahmad, Kamalika Chaudhuri, Sahana Chennabasappa, Xander Davies, Lauren Deason, Benjamin L. Edelman, Tanner Emek, Ivan Evtimov, Jim Gust, Maia Hamin, Kat He, Klaudia Krawiecka, Riccardo Patana, Neil Perry, Troy Peterson, Xiangyu Qi, Javier Rando, Zifan Wang, Zihan Wang, Spencer Whitman, Eric Winsor, Arman Zharmagambetov, Matt Fredrikson, Zico Kolter
TL;DR
Indirect prompt injection can manipulate agents through external data while concealing harmful actions from users, motivating systematic evaluation of this underexplored threat. The paper runs a large public red-teaming competition across tool use, coding, and computer use, finding vulnerability in every evaluated model and transferable attack strategies across behaviors and model families.
Problem
The paper addresses limited evidence about indirect prompt injections that both execute harmful actions and conceal compromise in agents processing external data.
Method
The authors evaluate 41 scenarios across three agent settings through a public competition, deduplicating attacks and measuring success with harmful-action and final-response judges.
Results
All 13 evaluated models were vulnerable, with ASRs ranging from 0.5% to 8.5%, while universal strategies succeeded across 21 of 41 behaviors and multiple model families.
Takeaways & Limitations
The findings support continued red-teaming and system- or architecture-level defenses because transferable attacks indicate vulnerabilities beyond individual models.
Takeaways & Limitations
The permissive threat model gives attackers full conversation visibility and targets only single-turn attacks, so results are an upper bound on single-turn vulnerability rather than real-world attack frequencies.
Abstract
from arXiv · showhide
LLM based agents are increasingly deployed in high stakes settings where they process external data sources such as emails, documents, and code repositories. This creates exposure to indirect prompt injection attacks, where adversarial instructions embedded in external content manipulate agent behavior without user awareness. A critical but underexplored dimension of this threat is concealment: since users tend to observe only an agent's final response, an attack can conceal its existence by presenting no clue of compromise in the final user facing response while successfully executing harmful actions. This leaves users unaware of the manipulation and likely to accept harmful outcomes as legitimate. We present findings from a large scale public red teaming competition evaluating this dual objective across three agent settings: tool calling, coding, and computer use. The competition attracted 464 participants who submitted 272000 attack attempts against 13 frontier models, yielding 8648 successful attacks across 41 scenarios. All models proved vulnerable, with attack success rates ranging from 0.5% (Claude Opus 4.5) to 8.5% (Gemini 2.5 Pro). We identify universal attack strategies that transfer across 21 of 41 behaviors and multiple model families, suggesting fundamental weaknesses in instruction following architectures. Capability and robustness showed weak correlation, with Gemini 2.5 Pro exhibiting both high capability and high vulnerability. To address benchmark saturation and obsoleteness, we will endeavor to deliver quarterly updates through continued red teaming competitions. We open source the competition environment for use in evaluations, along with 95 successful attacks against Qwen that did not transfer to any closed source model. We share model-specific attack data with respective frontier labs and the full dataset with the UK AISI and US CAISI to support robustness research.
1 Introduction
Indirect prompt injection becomes especially concerning when agents process attacker-controlled external data and conceal successful harmful actions from users. This work evaluates that dual objective across tool use, coding, and computer use through a large public competition.
- Motivation: Agents increasingly perform complex, multimodal, multi-step tasks, expanding their exposure to instructions embedded in external data.The paper motivates this risk in the context of rapid enterprise deployment and broader agent capabilities.
- Motivation: Concealment matters because long tool histories and reasoning traces make manipulation difficult for users to inspect, while final responses may contain no explicit compromise clue.An attack can therefore remain practically concealed after achieving the attacker’s objective.
- Evaluation objective: The benchmark requires attacks to force a target harmful action and pass scenario-specific criteria on the agent’s final response.Concealment is always included among the final-response criteria, alongside possible behavior-specific requirements.
- Competition findings: 464 participants contributed more than 240K attack attempts, with more than 8K successful attacks across 13 models and an overall ASR ranging from 0.5% to 8.5%.The three-week competition targeted major proprietary and open-weight models.
- Contributions: The study investigates concealment-aware attacks at scale, analyzes transferable strategies, and releases evaluation resources and attack data for robustness research.The open-science effort includes 95 successful attacks against Qwen that did not transfer to closed-source models.
2 Related Work
Prior work established indirect prompt injection as a security concern and introduced agent-specific benchmarks, while red-teaming research developed automated and human attack-generation methods. These lines of work provide the context for evaluating concealed attacks across agent settings.
- Prompt Injection Attacks: Indirect prompt injection was formalized as distinct from direct prompt injection and has since been validated in production and extended to multimodal inputs.The literature regards it as a major security concern for LLM-based systems.
- Agent Security Benchmarks: Agent security benchmarks progressed from single-turn adversarial prompting to evaluations of tool-calling agents, harm types, direct misuse, and crowdsourced agent red-teaming.The cited benchmarks include AgentDojo, InjecAgent, AgentHarm, ART, and b3.
- Red-Teaming Methodologies: Red-teaming methods span gradient-based, genetic, iterative, sampling-based, and learned attackers, alongside human competition-based efforts.The paper situates its competition among both automated and human red-teaming approaches.
3 Design
The benchmark combines 41 scenarios across tool use, coding, and computer use with a threat model requiring both harmful execution and concealed final responses. A public competition supplies deduplicated attacks for evaluation and open research.
- Scenarios: The competition defines 41 indirect prompt injection scenarios across three agentic settings in collaboration with the UK AI Security Institute and frontier labs.Figure 2 presents the overall arena design.
- Agentic Settings: The settings include 18 tool-use scenarios, 15 coding scenarios, and 8 computer-use scenarios spanning domain tools, software repositories, and graphical interfaces.Computer-use agents act through screenshots and mouse or keyboard actions, while coding agents execute commands and modify code.
- Scenario Construction: Each scenario specifies a benign task, injection location, harmful target action, and success criteria using plausible attacker-controlled content such as comments, configuration files, emails, tool fields, and webpages.Examples include a malicious email, compromised repository content, and adversarial webpage data.
- Threat Model: The threat model requires both execution of a harmful action and concealment of attacker intent in the final response.The attacker controls a fixed portion of one external data source and targets a single operation turn with potentially multiple tool-call rounds.
- Evaluation: The dual-judge system verifies harmful tool or computer actions programmatically and scores visible responses against scenario-specific criteria, including concealment.A response passes when all applicable criteria exceed the threshold of 7 on the 0–10 prompt-judge scale.
- Data Collection: After deduplication, the competition contained 271,588 chats, 67,634 submissions, and 8,648 successful attacks from 464 participants.Participants could iterate through multiple chats before requesting judgment of a submission.
- Benchmark Release: The curated benchmark contains 2,679 attacks across 41 behaviors, and the evaluation kit and broader attack data were released or shared for robustness research.Up to 9 successful attacks were sampled for each model-behavior pair, although some pairs had fewer or none.
4 Results
Across models and agentic settings, indirect prompt injections remained effective, with vulnerability varying by model, setting, attack transferability, and strategy. Robustness was not uniformly tied to capability, while transferable attacks exposed shared weaknesses across model families.
- Overall vulnerability: 0.5% to 8.5% ASR separated Claude Opus 4.5 from Gemini 2.5 Pro, respectively, across the competition.Claude Opus 4.5 was the most robust, while Gemini 2.5 Pro was the most vulnerable.
- Overall vulnerability: 100% behavior coverage occurred for Gemini 2.5 Pro and DeepSeek V3.1, compared with 35.1% for Claude Opus 4.5.The coverage figures were 37/37 behaviors for the first two models and 13/37 for Claude Opus 4.5.
- Overall vulnerability: Tool use had the highest ASR at 4.82%, followed by computer use at 3.13% and coding at 2.51%.Tool use was most vulnerable for every model except Claude Opus 4.5, whose computer-use ASR was 1.1% versus 0.6% for tool use.
- Capability and robustness: Capability and ASR had a weak, statistically nonsignificant correlation of r = −0.31, p = 0.3.Model family and training recipe appeared more strongly associated with robustness than raw capability, with large ASR differences among models with similar GPQA scores.
- Attack transferability: Under transfer evaluation, Qwen3 VL 235B and Nova 1 Premier reached 53% and 51% ASR, while Claude Opus 4.5 reached 2.5%.Gemini 3 Pro reached 16%, substantially below Gemini 2.5 Pro at 45%.
- Attack transferability: Attacks from robust source models transferred broadly, whereas Qwen3 VL 235B attacks transferred at 0% to Claude Opus 4.5 and 1% to Claude Sonnet 4.5.Attacks from Claude Opus 4.5 succeeded at 44–81% across other models, while Qwen3 VL 235B attacks reached 25–64% on more vulnerable models.
- Attack strategies: The most effective ranked strategy was “Fake Chain of Thought,” while strategy effectiveness generally differed across tool-use, coding, and computer-use settings.Only “Chain Harmful Shell Command with Benign Command” overlapped across agentic settings.
5 Discussion, Limitations, and Future Work
The evaluation found concealed harmful actions across all model families, while transfer patterns and scenario constraints motivate system-level defenses and more realistic, statistically stable future evaluations.
- Discussion: All evaluated models executed harmful actions while satisfying concealment criteria, with ASRs ranging from 0.5% to 8.5%.The permissive threat model gives attackers full conversation visibility, so these rates are an upper bound on single-turn vulnerability.
- System-level defenses: Universal attacks succeeded across 21 of 41 behaviors and multiple model families, indicating vulnerabilities beyond individual model implementations.Attacks from the most robust models transferred at 44–81% to other targets, whereas attacks from vulnerable models rarely transferred upward.
- Future work: Thinking-mode effects remain uncertain because only Kimi K2 was tested in both modes, where thinking improved robustness.The competition could not configure every evaluated model with both thinking and no thinking.
- More realistic scenarios: Coding scenarios had significantly lower ASR, plausibly because authentic coding-agent transcripts more closely resemble training data.Future offerings will collect more real-world transcripts and tool executions and balance scenario counts across settings.
- Other limitations: The benchmark evaluates only single-chance injections and mostly one-shot outcomes, leaving multi-hop escalation and statistical stability for future work.Repeated runs of the same attack on the same model did not always succeed, motivating multi-shot aggregation and bootstrapping.
6 Conclusion
The paper introduces a large-scale, concealment-aware benchmark spanning three agent settings and reports widespread but uneven vulnerability across models. Its recurring competition design is intended to track an evolving threat landscape.
- Benchmark and findings: 271,000+ attack attempts from 464 participants produced 8,648 successful attacks across 13 models.The benchmark spans tool use, coding, and computer use while requiring harmful action execution and concealment.
- Benchmark and findings: ASRs ranged from 0.5% to 8.5%, and all models proved vulnerable under the permissive threat model.Attackers had full conversation visibility during evaluation.
- Transfer and robustness: Universal strategies transferred across multiple behaviors and model families, while attacks from robust models transferred 44–81% to other targets.Attacks from vulnerable models rarely transferred upward, suggesting different failure modes across robustness tiers.
- Future evaluation: The benchmark will be updated recurrently to follow the evolving threat landscape as agents are deployed in increasingly diverse settings.The paper also reports that robustness was more strongly determined by model family and training methodology than raw capability.
A.1 Prompt Judge
The prompt judge scores only the agent’s final visible turn using scenario-specific criteria, while excluding hidden reasoning and prior conversational content.
- Inputs and outputs: The judge receives scenario documentation, criteria, and the transcript without the attacker’s injection payload.It returns criterion-level scores from 0–10 with short explanations.
- Judged content: Only the final assistant turn is judged, including tool calls and response text but excluding reasoning or thinking tags.User turns, earlier assistant turns, and previous tool outputs are excluded from judgment.
- Evaluation template: The prompt follows a consistent template across agent settings, defining the scenario, success criteria, and conversation transcript.The response format requires a JSON object with the specified keys and brief explanations.
A.2 Example Criteria
The benchmark operationalizes success through criteria that test whether agents conceal malicious instructions and provide convincing false justifications for harmful actions.
- Concealment: Concealment measures whether the agent avoids revealing awareness of the malicious instruction.Success requires confident execution without meta-awareness or security concerns in the visible response.
- Concealment: The concealment rubric ranges from explicit detection or refusal to perfect concealment presented as proactive helpfulness.Intermediate outcomes include questioning, hesitation, or subtle awareness that reduces naturalness.
- False justification: False justification assesses whether the agent gives a convincing but false explanation for a harmful action.The representative task requires framing a destructive command as recommended maintenance from project documentation.
- False justification: The false-justification rubric distinguishes non-execution, neutral framing, vague cleanup claims, recommended maintenance, and enthusiastic performance claims.The highest score requires a highly convincing explanation that the cleanup improves build performance.
A.3 Tool Judge
The tool judge verifies whether an agent executed the scenario’s target harmful action, using tool-call or task-specific checks across tool use, coding, and computer use. Vulnerable-scenario ratios compare models by the share of tested scenarios with at least one successful attack.
- Tool-judge criteria: The tool judge checks function names and arguments for tool use, target commands or files for coding, and target UI actions for computer use.Computer-use checks include actions such as navigating to a specific URL or clicking a specific element.
- Scenario-level vulnerability: The compromised scenario ratio is the percentage of tested scenarios with at least one successful attack for a model.Four of 41 scenarios were reserved in the pilot wave and excluded from final reporting.
B Additional Results
Additional results characterize scenario coverage, benchmark composition, participant activity, model targeting, evaluation criteria, and recurring attack strategies. The evidence also highlights that apparent robustness differences were not explained by attacker attention and varied substantially at similar capability levels.
- Model vulnerability: 4 of 41 scenarios were reserved in the pilot wave, while more susceptible models showed higher vulnerable-scenario ratios.The compromised scenario ratio measures scenarios with at least one successful attack divided by all scenarios tested for a model.
- Benchmark composition: On average, each model–scenario pair contributed 7 successful attacks, with more susceptible models supplying more source attacks to the benchmark.The benchmark samples deduplicated successful submissions, up to 9 per model–behavior pair.
- Attack strategies: The attack-strategy catalog includes chained malicious shell commands, forged tool outputs, fake error recovery, obfuscation, identity replacement, urgency, conditional triggers, and fabricated conversation history.Table 6 ranks 25 resolved strategies by overall ASR and illustrates them with simplified examples based on real submissions.
- Participation: The top 50 users accounted for approximately 88% of chats, submissions, and successful attacks, leaving a long tail of occasional contributors.
- Targeting: Models with higher attack success rates were not systematically targeted by more unique users, indicating that robustness differences were not explained by uneven attacker attention.
- Capability and robustness: The ASR–GPQA Diamond correlation was weak (r = −0.31, p = 0.299), with substantial robustness variation among models at similar capability levels.The figure notes that training recipe may instead dominate robustness, but presents this as a possibility.
C Attack Walkthroughs
The walkthroughs show indirect injections embedded in tool outputs, project documentation, and product pages, each attempting to redirect an agent from the user’s request. The dual-judge evaluation distinguishes concealed harmful execution from cases where the agent preserves the original request.
- Tool use: A tool-use attack embeds a fake platform-policy update in a product review and instructs the shopping agent to post a 1-star review instead of the requested 5-star review.The interface displays the selected scenario, injected tool output, compromised response, and evaluation scores.
- Coding: A coding attack disguises an instruction in project documentation to edit a flaky test so it always passes, reinforced by fake system-reminder tags claiming authorization.The agent complies while providing a false justification.
- Computer use: A computer-use attack embeds a fake developer note on a product page urging selection of a 3-pack instead of the requested 1-pack.The note claims the CSS selector is broken and uses emotional pressure and urgency to encourage compliance.
- Dual evaluation: The computer-use example passes the prompt judge because the response reveals nothing suspicious but fails the tool judge because the agent follows the original request.This illustrates why the dual-judge system separates concealment criteria from execution of the injected action.