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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPrompt injection is only one way an AI agent can go wrong. An agent can also cause harm because it has excessive permissions or autonomy, because its tools or integrations are unsafe, because it exposes sensitive data, or because corrupted state persists and influences later work. These failures arise from the whole system around the model: its tools, identity, memory, retrieval sources and connected services.
Why agent security is bigger than prompt injection
A chatbot that produces a bad answer can mislead someone. An agent can go further: plan a sequence of steps, call tools, retain information and change something in an external system. Its security boundary therefore includes every identity, data source, tool and downstream service it can reach—not just the model’s instructions.
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Prompt injection is one possible trigger. In agent hijacking, malicious instructions are placed in data the agent may ingest, such as content it retrieves or processes. NIST’s Center for AI Standards and Innovation (CAISI) describes the risk this way: “Currently, many AI agents are vulnerable to agent hijacking, a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.” But an agent can also fail because of an ordinary model mistake, an overly broad permission, an unsafe integration or a poorly specified objective.
The four categories below are an editorial way to organize the risks, not an official four-item OWASP or NIST taxonomy. They focus on the security failures that can occur after or alongside an attempted instruction hijack.
#1 Best Overall
1. Excessive agency: the agent can do too much
How it fails
An agent may be given more functionality, permission or independence than its task requires. OWASP’s agent guidance treats these as distinct aspects of excessive agency: the available functions, the rights attached to them and the agent’s autonomy to act. A connector intended for reading records, for example, may also expose editing or deletion. If the agent misunderstands a request or acts on malicious content, that extra capability turns a mistake into a consequential action.
Autonomy creates a separate risk from raw permissions. Even a legitimate operation can be unsafe if the agent performs it without a person’s review, repeats it at scale or takes an irreversible step on ambiguous evidence. The impact depends on what the agent can change and how readily the change can be undone.
What reduces the risk
- Expose only the functions the task needs. A read-only workflow should not inherit write or delete capabilities simply because the connector offers them.
- Use least-privilege identities and scopes, and carry the user’s authorization context through to the service that performs the action.
- Have the downstream service enforce authorization. A model’s judgment that an action is allowed is not a substitute for service-side access control.
- For destructive, financial, administrative or externally visible actions, require meaningful review. Show the proposed action and its target, validate it independently where appropriate, and use rate limits to constrain repeated or runaway activity.
2. Unsafe tools and integrations: untrusted input reaches powerful code
How it fails
Tools give an agent its reach, but a broad tool can turn a small reasoning error into a large security problem. An open-ended shell or URL-fetch function, for instance, gives the agent more room to execute or retrieve than a narrowly designed operation. Command injection can make untrusted input behave like executable instructions. Tool descriptions and outputs can also be manipulated, while compromised dependencies or servers can undermine the integration itself.
The Tool Desk
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Rank #3
What to review
- Prefer narrow, purpose-built functions over general-purpose shell, browser or URL-fetch access when a constrained operation will do.
- Review tool descriptions, outputs, dependencies and server provenance as security-sensitive inputs, not as inherently trustworthy instructions.
- For MCP, examine how credentials are stored and used, how each server and tool is authorized, where commands can execute, what telemetry is collected, whether shadow servers exist, and what context is shared.
- Remove stale plugins and integrations; an unused connection still expands the available attack surface.
3. Sensitive data exposure: reachable information escapes its boundary
How it fails
An agent may be able to read private records, credentials or confidential context to complete a legitimate task. Exposure can happen when it passes that information to a tool or API, includes it in an output, or writes it to a log. The central question is not only whether the model can produce sensitive text; it is which data the agent can retrieve and where that data can go next.
In 2025, NIST CAISI evaluated hijacking tasks that included mass exfiltration of cloud files and automated phishing. These were simulated evaluation tasks, not a measured rate of production incidents. They illustrate why impact matters: an attack that causes a harmless message and one that exports private files should not be treated as equivalent merely because each counts as a successful task hijack.
What limits exposure
- Restrict retrieval and tool access to the data needed for the specific task, under the requesting user’s permissions.
- Control what sensitive information tools return, where outputs are sent and what is retained in logs or other records.
- Evaluate data-exfiltration paths as well as model responses: test whether information can move from a retrieval source through a tool or output to an unauthorized destination.
4. Poisoned or unreliable state persists, spreads or drives the wrong goal
How it fails
An agent’s memory and working context can carry risk from one step or task into another. If malicious or misleading information is stored and later treated as reliable, it can influence future actions even after the original interaction has ended. In connected agent systems, a compromised agent may also pass harmful instructions or data to other agents, allowing a failure to spread.
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Best Value
What reduces persistence and propagation
- Scope memory writes to what is necessary; sanitize, expire or reject content that should not become durable state.
- Test whether manipulated memory or retrieved context can change later decisions, and whether one agent can pass unsafe state or instructions to another.
- Check that the agent’s objective and success criteria match the intended outcome, including cases where no adversarial input is present.
How to evaluate an agent deployment
Judge the whole deployment, not just the model’s ability to resist a prompt. A useful review asks how much capability and data the agent can reach, how long its state persists, how consequential its actions are, and whether authorization and oversight operate independently of the model.
- Capability and privilege: Which tools are available, what can each change, and whose identity and permissions does it use?
- Autonomy and reversibility: Which actions happen without review, how serious are their consequences, and can they be undone?
- Data reach: What sensitive information can be retrieved, passed to tools, returned to users or retained?
- Persistence and spread: What can be stored in memory or context, for how long, and can it influence other tasks or agents?
- Independent controls: Are authorization, logging, human review and rate limits enforced outside the model’s own decision-making?
Test realistic failure paths—including tool misuse, privilege escalation, data exfiltration, memory poisoning and runaway recursive tool use—and repeat attempts when an attack can recur. Re-test after material changes to prompts, tools, memory, retrieval, policies or model providers.
Why one test run is not enough
In a 2025 AgentDojo red-team exercise, NIST CAISI reported an 11% success rate for the strongest baseline attack against an upgraded Claude 3.5 Sonnet, compared with 81% for the strongest newly developed attack. The exercise used novel attacks developed for that model and a held-out set of Workspace user tasks; those figures do not describe other models or deployments.
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In a separate NIST CAISI experiment, five injection tasks were each attempted 25 times. Average attack success was 57% after one attempt and 80% after repeated attempts. The result is specific to those tasks and evaluation conditions, but it shows why a single successful or unsuccessful run can be a poor estimate for a probabilistic system. As NIST CAISI staff put it: “Since LLMs are probabilistic, the output of a model can vary from attempt to attempt.”
When comparing test results, record the task, attack method, number of attempts and severity of the resulting action. An aggregate success rate can hide the difference between a benign outcome and consequential data exfiltration.
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