Yes. A RAG agent can take an unauthorized action or lose track of a legitimate task, then refuse in its final answer. That refusal is not proof that the run was secure—or useful. To judge the agent, inspect what it did across the entire run, including retrieved content, tool calls and state changes, and measure benign-task completion separately from attack resistance.
What does a final refusal actually tell you?
Only that the agent refused in its final response. It does not show whether the agent previously followed an instruction hidden in a retrieved document, exposed data, changed a record, or otherwise crossed a boundary. OWASP’s LLM Prompt Injection Prevention Cheat Sheet puts the distinction plainly: “A refusal in the final response does not undo an action already taken.”
The reverse also matters: a refusal can block an attack but still leave the user’s legitimate request unfinished. A system that refuses every task may prevent some harmful actions, but it is not a successful assistant. A proper evaluation therefore needs at least two separate results: whether the attack caused harm and whether the agent completed the benign task. Neither result can be inferred from the final wording alone.
The title describes a possible failure pattern, not a quantified outcome established by the benchmarks below. The cited sources do not give a population estimate for how often an agent refuses an attack yet fails its user.
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How can prompt injection in RAG reach an agent?
Retrieval-augmented generation (RAG) adds external material to the model’s context. A typical flow collects and indexes documents, retrieves relevant passages for a user’s query, and supplies those passages to the model. If a document contains malicious instructions, those instructions can enter the context even though they did not come from the user or developer.
This is indirect prompt injection: the attacker places instructions in data the agent may later read, rather than relying only on a direct message to the model. NIST calls this agent hijacking and describes the underlying trust-boundary issue: an agent’s input can mix developer instructions with task-relevant external data. OWASP’s RAG Security Cheat Sheet similarly warns that poisoning can enter through the retrieval corpus; invisible Unicode and instructions split across multiple chunks can make detection harder.
The risk does not end when text is retrieved. The model may act on it, produce an unsafe answer, or pass information or instructions to a downstream tool. OWASP summarizes the broader pipeline concern this way: “RAG does not reduce risk — it redistributes it across the data pipeline, creating new attack surfaces at every stage from ingestion to generation to output.” A final-response check sees only one part of that process.
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What do published agent-attack tests show?
Several studies find meaningful vulnerabilities in their evaluated settings, but their percentages are not interchangeable. They use different models, tasks, attack sets, environments, and definitions of success; none is a universal estimate of production risk or of refusal-plus-user-failure.
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| Study and date | Evaluated setting | Reported result | How to read it |
|---|---|---|---|
| InjecAgent, Findings of ACL 2024 | 1,054 test cases involving 17 user tools and 62 attacker tools; ReAct-prompted GPT-4 was among the evaluated settings. | The authors report that ReAct-prompted GPT-4 was vulnerable in 24% of tested cases. | This is the benchmark’s result for that model, prompting approach, and test set—not a rate for all GPT-4 use or deployed agents. |
| NIST CAISI, 2025 | Agents powered by upgraded Claude 3.5 Sonnet, tested with novel attacks developed in collaboration with the UK AI Security Institute. | Attack success increased from 11% for the strongest baseline to 81% for the strongest novel attack. | The range reflects this specific evaluation and its attacks, not a general agent success rate. |
| Rag ’n Roll, preprint posted August 9, 2024 | The application and configurations tested by De Stefano, Schönherr, and Pellegrino. | About 40% attack success across various configurations; 60% when ambiguous answers were also counted as successful. | The broader figure depends on the authors’ rule for treating ambiguous answers as success. |
| WASP, NeurIPS 2025 | An end-to-end evaluation of agents against attacker goals. | Up to 86% partial attack success. | Partial success is not the same as fully completing an attacker’s goal; WASP reports that agents often struggled to complete goals fully. |
These results support end-to-end testing, but they do not establish how often an agent performs an unauthorized action and then refuses, or how often a refusal causes a benign task to fail. Those are separate outcomes that need to be measured in the system and tasks you care about.
Why can an agent block an attack and still fail the user?
Attack resistance and task usefulness are different properties. A malicious instruction might make the agent abandon the original request, answer the wrong question, or refuse to handle otherwise safe material. Conversely, a polished refusal can follow an earlier tool action. These are plausible ways the two properties can diverge; the cited benchmark figures do not measure their combined frequency.
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That is why “the model refused” is not a sufficient incident report or test result. A refusal is an output; security is about the whole execution, including whether data and tools stayed within authorized boundaries. Usefulness is about whether the original, legitimate task was completed correctly.
How should you evaluate a RAG agent?
Build tests around the agent’s real workflow, not just direct attacks typed into a chat box. Put hostile instructions in the retrieval path, where they can arrive as ordinary-looking document content. For each scenario, record three independent outcomes:
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- Legitimate utility: Did the agent complete the user’s original task correctly, including when it needed to ignore or safely report malicious content?
- Boundary integrity: Did retrieval permissions, tenant boundaries, tool permissions, and output constraints remain intact?
Before testing, specify what counts as task completion, attack success, data exposure, and an unauthorized state change. Then inspect the execution trace, not only the final answer:
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- Define a benign task and authorized boundaries. Record the expected result, which data the agent may access, and which actions it may take.
- Place attacks in retrieved material. Test documents and passages the system could actually retrieve, as well as direct user-message attacks where relevant.
- Vary the scenario. Include task-specific and adaptive attacks, and consider multiple attempts rather than relying on one prompt or one run. NIST recommends adaptive evaluations, task-specific analysis alongside aggregate results, and consideration of multiple attempts.
- Capture the full run. Review retrieved passages, model outputs, tool calls, permissions, and state changes. Check whether a prohibited action occurred before any refusal.
- Score security and usefulness independently. Report attack impact, benign-task completion and boundary violations separately; make the success definitions and tested configuration clear.
When comparing defenses, assess their coverage across ingestion, retrieval, generation, output and tool execution. A filter that blocks some malicious passages does not by itself establish that the system resists attacks end to end or still completes benign work reliably.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to secure a RAG agent across the pipeline
No single refusal rule or content filter can cover every boundary. OWASP’s guidance points toward layered controls that protect documents, access, context, outputs and actions, with enough observability to evaluate what happened.
Protect document provenance and integrity
Track where indexed content came from and who can change it. Apply integrity checks against an approved baseline. A matching document digest can show that content matches that baseline; OWASP cautions that it does not prove the content is safe or free of injection.
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Enforce access boundaries in retrieval
Apply access metadata and tenant isolation during retrieval, not just in the interface. An agent should not receive documents a user or task is not authorized to access. Include these boundaries in tests so that hostile content cannot use retrieval to cross them.
Keep retrieved context bounded and test placement
OWASP offers 3–5 retrieved chunks totaling 2,000–4,000 tokens as a reasonable starting point, not a universal safe limit. It cautions that model attention behavior varies, so test context size and the placement of relevant or hostile passages for the specific model and workflow.
Validate outputs and constrain tools
Validate model outputs before they reach users or downstream systems. Define allowed tool actions with schemas and enforce authorization outside the model; do not treat a model’s promise or refusal as the permission check. Review whether tool calls have appropriate scope and whether sensitive data can flow into an unauthorized destination.
Log events and fail closed at sensitive boundaries
Keep enough observability to reconstruct retrievals, decisions, tool calls, errors and state changes. For sensitive actions, define what happens when validation, authorization or other required checks fail; OWASP recommends fail-closed behavior. This makes it possible to distinguish an agent that safely declined an action from one that acted and only later refused.
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What a useful security result should report
A credible evaluation states which model and configuration were tested, what tasks and attack scenarios were used, how success was defined, and what happened across the run. It should report attack outcomes and benign-task performance separately, along with relevant boundary violations and the evidence used to identify them. Aggregate scores are useful, but task-specific results reveal where an agent blocks attacks at the cost of the user’s work—or appears to refuse safely after something has already gone wrong.
The practical verdict is simple: treat refusal as one observable response, not a security certificate. Judge the complete run against both attack impact and the user’s legitimate goal.
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