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AI agents can be manipulated by malicious instructions hidden in ordinary content they process, such as an email, file, webpage, or tool response. Whether that manipulation causes harm depends less on the model alone than on what the agent is allowed to access, change, send, or execute. Defenses can reduce that risk, but they need to enforce limits outside the model—with restricted permissions, downstream authorization, isolation, network controls, and human review for consequential actions.
How can an AI agent be manipulated across a security boundary?
Agents often combine developer instructions with task data. If that data contains an instruction such as “send this file to this address” or “run this program,” the model may treat it as a direction rather than as untrusted content to analyze. When the agent has tools, it may then attempt to carry out the instruction. NIST’s Center for AI Standards and Innovation (CAISI) calls this kind of attack agent hijacking, a form of indirect prompt injection.
The content itself does not need special access to the agent. It can arrive through an email the agent is asked to summarize, a document it is asked to read, a webpage it visits, or a response from a connected tool. The vulnerability is the path from content to action: the model interprets the content, and the agent’s tools provide a way to act on that interpretation.
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OWASP’s risk guidance also covers direct prompt injection, tool abuse, privilege escalation, data exfiltration, memory poisoning, goal hijacking, approval manipulation, cascading failures between agents, supply-chain attacks, sensitive-data exposure, and runaway compute costs. These are different failure modes, but they share an important distinction: a model’s susceptibility to an instruction is only part of the risk. Permissions and execution pathways determine what a manipulated agent can affect.
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What evidence shows that agent-hijacking defenses can fail?
In a 2025 report, NIST CAISI described an evaluation of agents powered by Anthropic’s upgraded Claude 3.5 Sonnet, released in October 2024. The team used AgentDojo environments and additional custom scenarios. On held-out tasks, CAISI measured an 11% success rate for its strongest baseline attack and an 81% success rate for its strongest novel attack.
Those numbers are attack-success rates in that particular evaluation—not estimates of how often real attackers succeed, a failure rate for all deployed agents, or a general score for current defenses. CAISI also described simulated scenarios involving downloading and running a program, sending cloud files to an unknown recipient, and sending personalized phishing messages. These examples show the kinds of actions the tested agent could be induced to attempt in the evaluation; they are not reports of real-world incidents.
The results are a reason to test how an agent behaves under adversarial input, not a basis for predicting the odds of compromise in a specific deployment. The available evidence does not establish how well the recommended controls perform across real-world deployments or whether any single control reliably prevents agent hijacking.
Which boundaries should a defense enforce?
Controls work at different points in an agent’s path from instruction to action. A useful design question is not simply whether a control exists, but which boundary it enforces and what happens if another layer fails.
| Boundary | Control to enforce it | What it limits |
|---|---|---|
| Identity and credentials | Agent-specific identity and short-lived, task-scoped credentials | Which identity can be held accountable and which resources its credentials can reach |
| Tool access and action authorization | Least-privilege tool permissions and authorization in the system that performs the action | Which actions can be requested and which requests the downstream system accepts |
| Execution and filesystem | Sandboxed, isolated runtime with narrowly scoped file access | What an agent or a tool it invokes can access on the host or in its environment |
| Network | Restricted outbound connections | Where data can be sent if the agent is manipulated |
| Consequential actions | Human approval for high-impact or irreversible operations | Whether an important action proceeds without a person’s review |
| Detection and assurance | External logging, alerts, and repeatable adversarial tests | Whether suspicious activity is visible and whether behavior is checked again after changes |
These layers address different failure paths. The cited guidance does not establish a universal substitute for the others or provide a common comparative effectiveness score.
How should teams limit what an agent can do?
Start with the smallest useful permission set
Deny access by default, then allow only the tools and resources the task requires. Separate read access from write access and use different tool sets for different trust levels. If an agent only needs to summarize mail, it should not also have the ability to send messages or forward attachments.
OWASP’s excessive-agency guidance illustrates the risk with a mailbox assistant that can both read and send mail: a malicious email could induce it to forward sensitive messages. The suggested mitigations include read-only access, removing unnecessary send functionality, and requiring manual approval before sending. This is a guidance scenario, not a claimed incident.
Give agents their own scoped identity
Use an attributable, revocable identity for the agent rather than a developer’s personal credentials. Prefer short-lived tokens scoped to a task, and keep production secrets out of prompts and agent environments. This reduces the reach and useful lifetime of credentials exposed to an agent or misused after a successful manipulation.
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Authorize actions where they take effect
Do not make the model the final authority on whether an action is allowed. Validate every tool request against policy in the system that performs the action, such as the service that sends a message or changes a record. Require human review for high-impact or irreversible operations. A model-generated explanation or a permission prompt does not replace enforcement at the action boundary.
How can isolation and network controls contain a manipulated agent?
Isolate execution and file access
Run agents in an OS sandbox, development container, disposable virtual machine, or cloud environment configured for the task. Avoid production credentials and broad mounts of a user’s home directory. Check which components actually fall inside the boundary: file tools and MCP servers may have access beyond what the main agent runtime appears to expose.
OWASP’s DevSecOps guideline puts the distinction plainly: “Permission prompts are not a security boundary against a manipulated agent; isolation is.” A prompt can ask for approval, but it does not contain a process that is already able to reach sensitive files or systems. Isolation limits that reach even when the model’s judgment fails.
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Restrict outbound network access
Allow connections only to destinations needed for the task. If an injected instruction succeeds, outbound restrictions can reduce the routes available for sending data elsewhere. Network controls complement tool permissions: they constrain a communication path that may remain available through an agent or an integrated tool.
How should teams vet tools and monitor agent activity?
Treat tools and their outputs as untrusted
Tool descriptions and responses can influence what an agent does, so they should not automatically be treated as trusted instructions. Maintain an approved registry of MCP servers, inspect requested permissions and code, pin versions, and sandbox local servers. Grant each integration only the access it needs.
Keep useful logs outside the agent’s control
Record tool calls, commands, file writes, network requests, the agent identity, and the outcome. Store logs somewhere the agent cannot alter, avoid recording secrets, and alert on unusual access or destinations. These records help a team investigate what the agent attempted and whether a boundary allowed the action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can teams test whether their boundaries hold?
Use a repeatable set of abuse cases rather than relying on a single demonstration or a general claim that an agent is safe. Test prompt overrides, tool misuse, privilege escalation, memory poisoning, data exfiltration, runaway action chains, approval bypass, and escalation between agents. Include the real tools, permissions, and execution environment that the deployed system uses; otherwise, a test may miss the path that matters.
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- Exercise the full path. Supply adversarial content through realistic sources and observe the agent, its tools, and downstream systems.
- Record outcomes. Retain the tested system version and whether requests were approved, denied, or timed out.
- Retest after material changes. Repeat the cases when prompts, tools, memory, retrieval, policies, or providers change.
OWASP’s agentic-application guidance recommends testing and security practices for agent systems; the NIST CAISI evaluation also demonstrates why novel attacks and held-out tasks matter. A passing test is evidence about the tested configuration and cases, not proof that an agent cannot be manipulated by other inputs.
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What public standards work is underway?
NIST’s AI Agent Standards Initiative, created on February 17, 2026 and updated on August 14, 2026, describes three pillars: facilitating industry-led standards, fostering community-led protocols, and investing in research. NIST lists work on agent authentication and identity infrastructure, as well as security evaluations. This is active initiative work, not a completed universal standard for securing agents.
OWASP’s Securing Agentic Applications Guide 1.0, published July 27, 2025, presents practical guidance for designing, developing, and deploying LLM-powered agentic applications. Such guidance can inform a defense program, but the available sources do not establish a deployment-wide effectiveness ranking for specific controls.
What should a deployment decision be based on?
Assess the complete path from untrusted input to consequential action: what identity the agent uses, which tools and resources it can reach, where authorization is enforced, what the runtime and network allow, which actions require human review, and whether activity and tests are recorded. A defense is only as useful as the boundary it actually enforces. The evidence supports layered safeguards and repeated testing; it does not support a claim that any one control—or current defense in general—will stop every agent-hijacking attempt.
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