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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAI agents can be redirected by malicious instructions hidden in ordinary content such as an email, file, webpage, or tool response. Whether that manipulation becomes a security incident depends on what the agent is allowed to access and do. Defenses can reduce the consequences, but they should enforce boundaries outside the model—with scoped identity, least privilege, downstream authorization, isolation, restricted network access, human review, and ongoing tests.
How an agent can cross a security boundary
An agent typically receives both instructions about its role and data needed to complete a task. If an email, document, webpage, or tool output contains hostile directions, the model may treat those directions as instructions and use its tools to act on them. This is called indirect prompt injection; NIST’s Center for AI Standards and Innovation (CAISI) describes the resulting redirection as agent hijacking.
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The model’s susceptibility is only part of the risk. Tools and permissions determine what a manipulated agent can affect. A hijacked agent with read-only access has a different reach from one that can send messages, change records, run programs, or reach production systems. OWASP’s agentic-application risk guidance also covers tool abuse, privilege escalation, data exfiltration, memory poisoning, approval manipulation, cascading failures between agents, supply-chain attacks, and runaway compute costs.
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What the risk looks like
In simulated scenarios, CAISI tested whether an agent could be induced to download and run a program, send cloud files to an unknown recipient, or send personalized phishing messages. These were evaluation scenarios, not reports of real-world incidents. OWASP’s excessive-agency guidance gives another illustrative case: a mailbox assistant that can read and send mail might be induced by a malicious email to forward sensitive messages.
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What the available test results show—and do not show
In a 2025 CAISI evaluation, the strongest baseline attack succeeded on 11% of held-out tasks, while the strongest novel attack succeeded on 81%. The evaluation used agents powered by Anthropic’s upgraded Claude 3.5 Sonnet, released in October 2024, in AgentDojo environments and additional custom scenarios. CAISI reported that its team was frequently able to induce the tested agent to follow malicious instructions across the new risk areas.
Those percentages are attack-success rates in that particular test setup. They are not estimates of how often agents are hijacked in production, a general score for deployed defenses, or a result that applies to every model and agent configuration. The sources reviewed do not establish how effective the listed controls are across real deployments or show that any one layer reliably prevents every hijacking attempt.
Which security boundaries to put in place
Use controls that limit different parts of an agent’s path from input to action. A model instruction or permission prompt can help guide behavior, but it is not a substitute for authorization and containment enforced by the systems around the model.
| Boundary | What to enforce | Practical implementation |
|---|---|---|
| Identity and credentials | Which identity acts, and what it can access | Give each agent an attributable, revocable identity. Prefer short-lived, task-scoped tokens; keep production secrets out of prompts and agent environments. |
| Tool permissions and authorization | Which actions are allowed on which resources | Start from deny; allow only task-required tools and resources. Separate read from write access, and validate each request in the downstream system that performs the action. |
| Execution and filesystem isolation | What the agent can reach if it runs code or misuses a file tool | Use an OS sandbox, development container, disposable VM, or isolated cloud environment. Avoid production credentials and broad home-directory mounts, and check whether file tools and MCP servers fall inside the isolation boundary. |
| Network egress | Where the agent can send data or requests | Allow only destinations required for the task, reducing routes for exfiltration if manipulation succeeds. |
| Human approval | Whether consequential actions need a person to authorize them | Require review for high-impact or irreversible actions, such as sending sensitive messages or making consequential changes. |
| Monitoring and testing | What the agent did, and whether boundaries still hold | Log tool calls, commands, writes, and network requests with agent identity and outcome. Keep logs outside the agent’s control, avoid recording secrets, and alert on unusual access or destinations. Re-run adversarial tests after material changes. |
Reduce permissions before adding more prompts
Give an agent only the tools and data needed for its task, and keep trust levels separate where possible. For a mailbox assistant, read-only access may be enough for summarizing or searching mail; remove sending functionality if it is not required. If sending is necessary, put approval before delivery rather than relying on the model to judge whether a message is safe.
Make authorization independent of the model
Check every tool request against policy at the service or system that carries out the action. A model can request an operation, but the downstream system should decide whether that identity may perform it on that resource. This preserves an enforceable boundary even when the model follows hostile content. OWASP’s DevSecOps guideline puts the distinction plainly: “Permission prompts are not a security boundary against a manipulated agent; isolation is.”
Contain execution and outbound access
Run agents without broad access to a developer’s files, credentials, or production environment. Verify what the chosen sandbox actually covers: a shell may be isolated while a separate file tool or MCP server is not. Restrict outbound network access to destinations necessary for the job so a compromised workflow has fewer ways to transmit data.
Trust tools and their outputs cautiously
Treat tool descriptions and responses as untrusted input, just as you would external documents. Maintain an approved registry of MCP servers, inspect their code and requested permissions, pin versions, and sandbox local servers. A trusted-looking tool description does not by itself make the tool or its returned content safe.
How to test whether controls hold
Maintain a repeatable set of abuse cases rather than relying on a one-time demonstration. OWASP’s guide recommends adaptive red-teaming; its risk areas also provide useful test categories.
- Prompt override through emails, documents, webpages, and tool responses.
- Tool misuse, privilege escalation, and attempts to bypass approval.
- Memory poisoning, data exfiltration, and unusual network destinations.
- Runaway action chains, escalating costs, and failures that propagate between agents.
Run the cases again after material changes to prompts, tools, memory, retrieval, policies, or providers. Keep a record of the tested version and the observed approvals, denials, and timeouts so a later change can be compared against a known result.
What NIST’s current initiative means
NIST created its AI Agent Standards Initiative on February 17, 2026, and its description was updated August 14, 2026. The initiative names three pillars: facilitating industry-led standards, fostering community-led protocols, and investing in research. Its listed work includes research into agent authentication and identity infrastructure and the development of security evaluations. This is active initiative work, not a completed universal standard. Separately, OWASP’s Securing Agentic Applications Guide 1.0, dated July 27, 2025, presents practical guidance for designing, developing, and deploying LLM-powered agentic applications.
How to compare defensive approaches
Compare controls by the boundary they enforce, not by a single security label or claimed score. Identity and credentials govern who acts; tool permissions and downstream authorization govern which actions are accepted; isolation and egress restrictions limit what a compromised process can reach; approval adds review for consequential operations; monitoring and repeatable testing help reveal failures and regressions. These layers address different failure paths, and the available sources do not provide a common effectiveness score that would make one a universal substitute for the others.
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