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Yes, but a malicious webpage cannot steal a secret merely by displaying hostile instructions. A leak requires a chain: the agent must interpret the page’s untrusted content, have access to the secret, and have a way to send it somewhere the attacker can reach. The agent’s permissions, tools, and safeguards determine how much risk that creates.
How a website prompt injection can lead to a leak
OWASP defines indirect prompt injection as external content—such as a website or file—affecting a model when it is interpreted. A webpage can therefore act as an untrusted instruction source even when the user only asked an agent to read or summarize it. The instruction does not have to be visible or readable to a person if the model can parse it. OWASP’s LLM01:2025 guidance describes this risk.
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For a secret to leave, several conditions generally have to line up:
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →- The page influences the agent. The agent reads and interprets attacker-controlled content as instructions rather than treating it only as data.
- The agent can access something sensitive. The relevant information might be in its conversation context, connected files, or an authorized system. If it is unavailable to the agent, the page cannot make the agent disclose it.
- The agent has a disclosure path. It needs some way to expose the information, such as generating an answer visible to an attacker, sending a message, following a link, or using a tool to transmit data.
OpenAI describes this as a source-and-sink problem: external content is the source of influence, while a transmission or tool action can provide the sink. OWASP also identifies sensitive-information disclosure and unauthorized function access as possible impacts. These are conditions for risk, not a guarantee that every injection attempt will succeed. A model may ignore the content, lack access to a secret, or be stopped by a control. OpenAI’s explanation of prompt-injection risks and OWASP’s guidance describe these paths.
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What published testing does—and does not—show
In the scenarios evaluated in the 2025 paper “WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks”, agents began executing adversarial instructions 16–86% of the time, while achieving the attacker’s goal 0–17% of the time, according to the WASP authors. The gap matters: beginning to follow an instruction is not the same as completing an attack.
Those ranges describe the tested agents and benchmark scenarios, not the probability that a random webpage will compromise any current AI agent. They should not be read as a rate of real-world secret theft or as a score for every product.
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What determines an agent’s exposure
When assessing an agent, focus on the path from untrusted page content to sensitive data and outward action. OWASP’s guidance and the defenses described by Google and OpenAI point to several useful questions:
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- What can the agent see? Consider whether it receives private conversation context, files, account information, or credentials. Keeping unnecessary secrets out of model-visible context limits what an injection could expose.
- What can its tools do? An agent with broad permissions has more ways to act than one given only the capabilities required for a task. OWASP advises granting only necessary tools; scoped or short-lived credentials are practical ways to reduce the consequences of misuse.
- Can it communicate or navigate freely? Unrestricted outbound messages, network requests, or link following can create additional disclosure paths. Constraints on destinations and transmission can reduce those opportunities.
- Are page content and privileged instructions separated? Treating external content as untrusted data, rather than letting it direct privileged planning, can make an attack harder to carry through.
- Do consequential actions require independent approval? A confirmation step for high-impact actions can interrupt an attack before it completes.
- Is there current evidence from adversarial testing? Public testing information can help explain what has been evaluated, but results should be judged by the systems and scenarios covered.
These are assessment questions, not a certification checklist: safeguards differ between products, and no single control establishes that an agent is immune.
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What specific vendors say about their safeguards
Vendor descriptions apply to their own systems; they should not be assumed to describe every AI browser or agent.
Google Chrome
In a Chrome Security article dated December 8, 2025, Google’s Nathan Parker called indirect prompt injection “the primary new threat facing all agentic browsers.” Google describes possible injection sources including malicious sites, iframe content, and user-generated content, and says an attack could prompt unwanted actions such as financial transactions or exfiltration of sensitive data. The article outlines Google’s layered approach: a separate User Alignment Critic, restrictions on the origins an agent can interact with, user confirmation for critical steps, real-time threat detection, and red-teaming. These are descriptions of Google’s approach, not a guarantee for other browsers. Google Chrome Security’s article
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OpenAI
OpenAI describes source-and-sink analysis and a Safe Url mitigation for its systems. According to OpenAI, Safe Url may show information proposed for transmission and request confirmation, or block the transmission. That description should not be generalized to unrelated agent products. OpenAI’s article
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OWASP recommends constraining an agent’s role, validating expected outputs, checking inputs and outputs, enforcing least privilege, requiring human approval for high-risk actions, clearly separating untrusted external content, and regularly testing against adversarial inputs. Its prevention guidance cautions that fool-proof prevention is unclear.
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OWASP also describes CaMeL as a promising design that separates privileged planning from quarantined parsing: a planner does not read risky documents, a parser has no tool access, and a separate interpreter tracks data flow to block unauthorized actions. OWASP says this approach remains early and needs further research and development for wide adoption; it is not a universally available product or a mature default. OWASP’s prevention guidance
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to reduce risk when using or building an agent
There is no universal fix, but reducing access and limiting what an agent can do can reduce either the likelihood of a leak or its impact. OWASP’s recommendations support these practical steps:
- Give an agent only the tools and permissions needed for its task; avoid broad or persistent credentials when narrower access will work.
- Keep sensitive information out of prompts and logs where feasible, and limit which private sources the agent can access.
- Treat webpage text and other external content as untrusted input, not as authority to override the user’s request or the agent’s operating rules.
- Constrain outbound communication and require independent human approval for high-risk or sensitive actions.
- Use input and output checks, validate expected results, and test defenses with dummy data in a sandbox rather than live secrets.
- Review public, product-specific security information rather than assuming that a safeguard described by one vendor exists in another agent.
OWASP notes that, given the stochastic nature of model behavior, it is unclear whether fool-proof prevention is possible. Controls should therefore be understood as risk reduction, not proof that prompt injection cannot work. OWASP’s LLM01:2025 guidance
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