Yes, but a malicious webpage cannot steal a secret simply by containing hostile instructions. The risk arises when an AI agent interprets that page, can access sensitive information, and has a way to send that information out—such as a tool, an outbound request, or a response. Whether an attack succeeds depends on the agent’s permissions, available context, and safeguards.
How a website prompt injection could expose a secret
OWASP defines indirect prompt injection as external content—such as a website or file—influencing a model when the model interprets it. A page can therefore supply instructions to an agent even when the user only asked the agent to read or summarize the page. The instructions need not be visible to a person if the model can parse them. OWASP’s LLM01:2025 guidance describes this risk.
A disclosure generally requires a chain of conditions:
- A hostile source influences the agent. The agent reads and interprets attacker-controlled page content.
- The agent can access something sensitive. The relevant secret must be available in its context or through an accessible tool.
- There is a path for the information to leave. The agent might include it in a response, transmit it to a third party, follow a link, or use a tool in a way that exposes it.
OpenAI describes this as a source-and-sink problem: external content is a potential source of influence, while a transmission or tool action can provide a sink. An injection attempt is not the same as completed theft. The agent may ignore the content, lack access to the secret, lack an outbound capability, or be stopped by a safeguard. Broader access and unreviewed outbound actions create more opportunity for harm. OpenAI’s account of prompt-injection defenses explains its source-and-sink framing.
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What published testing can—and cannot—tell you
The 2025 paper “WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks”, by Ivan Evtimov, Arman Zharmagambetov, Aaron Grattafiori, Chuan Guo, and Kamalika Chaudhuri, reports results from its evaluated scenarios:
| Outcome measured | WASP result | How to interpret it |
|---|---|---|
| Agents began executing adversarial instructions | 16–86% of the time | Starting to follow an injected instruction did not mean the attacker’s objective was achieved. |
| Agents achieved the attacker’s goal | 0–17% of the time | This measures completed objectives in the benchmark’s test setup, not a general-world leak rate. |
The ranges apply to the agents and scenarios evaluated by the WASP authors in 2025. They are not probabilities for every current AI agent, browser, or website, and they do not establish that most agents will leak secrets. The gap between beginning to follow an instruction and completing the attacker’s goal is important: susceptibility is not the same as successful exfiltration.
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How to reduce the risk when designing or choosing an agent
No single safeguard makes prompt injection impossible. OWASP says it is unclear whether fool-proof prevention methods exist, given the stochastic way models work. Its guidance emphasizes reducing the likelihood or impact of an attack through controls such as these:
- Limit access. Give the agent only the tools and permissions needed for its task. Keep credentials scoped and short-lived where practical, and avoid placing secrets in model-visible prompts or logs when feasible. These are design applications of least privilege, not guarantees.
- Separate untrusted content from privileged instructions. Treat webpages and files as data to analyze, not as trusted commands. Architectures that keep external content away from privileged planning can reduce the chance that page text directs tool use.
- Constrain outbound actions. Restrict where the agent can navigate or send information. Require independent human approval before high-impact actions, such as transmitting sensitive data or completing a transaction.
- Validate inputs, outputs, and actions. Use checks appropriate to the task, and monitor tool calls rather than relying only on the model to recognize malicious text.
- Test adversarially and safely. Regularly test how the system handles hostile content, using dummy data and sandboxed substitutes rather than live secrets.
OWASP’s prompt-injection guidance recommends measures including least privilege, output validation, human approval for high-risk actions, separation of untrusted content, and adversarial testing. Its agent-security guidance also identifies tool abuse, privilege escalation, and data exfiltration as related risks.
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Architectural isolation: CaMeL
OWASP’s prevention cheat sheet discusses CaMeL, an approach that separates privileged planning from parsing risky documents and tracks data flow so unauthorized actions can be blocked. In the described design, a planner does not read risky documents, a parser has no tool access, and a separate interpreter checks actions. OWASP calls the approach promising but says it remains early and needs further research and development for wide adoption; it is not a universally available product or mature default. OWASP’s prevention cheat sheet describes the approach.
Vendor safeguards are product-specific
Safeguards described by one vendor should not be assumed to exist in every agent or browser. OpenAI says its Safe Url mitigation may show information proposed for transmission and request confirmation, or block the transmission. That description applies to OpenAI’s own system; it is not evidence that unrelated products use the same mechanism. OpenAI’s explanation covers its approach.
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In a Chrome Security article dated 2025-12-08, Google describes indirect prompt injection in malicious sites, iframe content, and user-generated content. It outlines layers in its own approach, including a separate User Alignment Critic, limits on origins the agent can interact with, confirmation for critical steps, real-time threat detection, and red-teaming. These measures describe Google’s approach, not a guarantee across browsers. Google’s Chrome Security article provides the details.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to ask about an AI agent’s security
If you are evaluating an agent for work involving private data, assess the full route from webpage to possible disclosure—not just whether the vendor says it detects prompt injection.
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- What private context, files, accounts, or secrets can the agent access?
- Which tools and credentials can it use, and how broad are their permissions?
- Can it send data externally or navigate to arbitrary destinations? Are those actions constrained?
- Is untrusted page content isolated from privileged instructions or planning?
- Do sensitive or high-impact actions require confirmation independent of the model?
- Does the vendor provide current public information about adversarial testing and the safeguards used?
These questions reflect the attack paths and safeguards discussed in OWASP, Google, and OpenAI guidance. A “yes” to more access or fewer restrictions does not prove an agent is vulnerable, but it identifies where the consequences of a successful manipulation could be greater.
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