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Evaluate an AI agent as a complete system, not as a model responding to isolated prompts. Map the agent’s inputs, tools, identities, data access and trust boundaries; test whether hostile content can trigger unauthorized actions or expose data; then verify that execution-layer controls block those actions even if the model complies. Repeat the tests before launch and after material changes.
What an AI agent security evaluation must cover
An agent’s security depends on more than its model. Its behavior is shaped by the orchestration that routes requests, tools and APIs it can call, credentials it uses, data it can retrieve, memory that persists between tasks, and the outside content it consumes. A web page, email, document, tool response or MCP description can contain instructions that the model treats as relevant even when the content is untrusted.
The core evaluation question is therefore not just whether the model refuses a malicious user prompt. It is whether an attacker can influence the full workflow—from input, through model and orchestration, to a tool or data store—and cause an action that the agent should not be allowed to take. OWASP’s agent-security guidance emphasizes threat modeling the agent, its orchestrator, tools, sources and trust boundaries before testing.
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Assess two things together: whether the agent completes legitimate tasks reliably, and whether its capabilities remain bounded when inputs are adversarial. A refusal can be too weak if a tool call still executes; controls can also be too restrictive if the agent cannot perform its authorized work. Your release decision should account for both.
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Map the system and its trust boundaries
Inventory components and capabilities
Start with an architecture and capability inventory. Record each model and orchestration component, connected tool or API, permission scope, identity, credential, data store, retrieval source, memory component and communication channel. Include capabilities that are easy to overlook, such as sending email, creating a rendered link, writing logs, accessing an MCP server or passing information to another agent.
For each component, write down what information it can access and what changes it can make. A tool that reads a record has a different impact from one that edits or deletes it; a tool that can send a message outside the organization creates a possible disclosure path. Note which actions are available in each workflow, rather than describing the agent as having one undifferentiated set of permissions.
Trace every input to the actions it can influence
List direct and indirect inputs: user messages, uploaded files, web pages, emails, retrieval results, tool outputs, MCP resources and messages from other agents. Mark which are trusted, which are partly trusted and which are untrusted. For each input, trace whether it can influence the model’s instructions, retrieved context, memory, tool selection, tool arguments, or the final response.
This map exposes the paths that prompt-only testing misses. For example, a user can ask for a routine summary while an instruction embedded in a retrieved document attempts to redirect the agent toward an unrelated tool call. The relevant test is whether that document can influence the action—not simply whether the user’s original message looks benign.
Define intended tasks and consequences
Describe the agent’s authorized tasks in operational terms: what it may read, what it may change, who it may contact, and under what conditions it must seek approval. Identify the consequences of errors, including data disclosure, unwanted changes, external communications, financial actions or administrative changes. This turns broad phrases such as “safe” into testable boundaries.
Write down ordinary expected behavior before running attacks. A baseline shows whether the agent can complete its intended work and clarifies what counts as a security failure, an incorrect but harmless answer, or an unnecessarily blocked task.
Inspect identity, permissions and isolation
Use attributable, limited identities
Check that the agent acts through a separately attributable identity rather than a developer’s personal account. Prefer dedicated identities and credentials scoped to the task, with read-only access where it is sufficient. Credentials should be short-lived where practical, and the agent should not receive access to production secrets merely because the surrounding application can access them.
Inventory the permissions attached to each tool and API. Confirm that the scope matches the task and that one tool cannot silently inherit a broader identity than the agent needs. Test the boundary for every capability, including each MCP server; a permission prompt or a sandbox elsewhere in the system does not establish that all paths are protected.
Limit runtime and network reach
Run tests in a sandbox or disposable environment with limited filesystem access and network egress. Check what files the agent can read, where it can write, and whether it can make outbound requests to destinations outside the intended workflow. Keep production secrets and real sensitive user data out of adversarial test prompts and test environments.
Isolation reduces the consequences of a successful manipulation; it does not prove the agent is secure. Record the boundary being tested and verify that the deployed execution path uses the same relevant restrictions. If the test environment differs materially from production, an apparent pass may not transfer.
Run realistic abuse-case tests
Use repeatable scenarios based on the actual workflows and the capability map. Test normal operation first, then introduce adversarial content through the channels the agent consumes. OWASP’s agent-security material organizes testing around abuse cases such as instruction override, indirect injection, tool misuse, privilege escalation, data exfiltration, memory poisoning and runaway execution.
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Try instructions that tell the agent to ignore its governing directions, disclose hidden context, or take an unrelated action. Place them not only in user messages but also in retrieved documents, web pages, emails and tool responses. Observe whether the agent treats those directions as authoritative, changes its task, or tries to call a tool because of them.
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Keep the underlying task realistic: for example, ask the agent to summarize a document that contains hostile directions. The test is not whether it can recognize a conspicuous attack string in isolation; it is whether it maintains the requested task and respects its boundaries while processing untrusted content.
Tool misuse and privilege escalation
Test whether the agent requests a tool, target or argument that is outside the user’s task or its own authority. Try a low-trust input that asks it to reach privileged data or invoke an administrative capability. Check both the model’s decision and the actual execution result. A tool request that is rejected by an independent authorization layer is a different outcome from a request that reaches the target and is merely followed by a model apology.
Data exfiltration
Test whether sensitive context can be induced to leave through any available channel: a tool, email, network request, rendered link, citation, log or final answer. Track the information flow rather than looking only for a direct verbatim secret in the response. An agent may disclose information indirectly or pass it to a tool even if its final message appears restrained.
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Memory poisoning and cross-task effects
If the agent retains information across turns or tasks, test whether malicious content can persist and influence later work or another user’s session. Check whether stored material is validated, scoped to the right user or task, and subject to an appropriate expiry. A one-session test will not reveal a problem that appears only after the agent has carried hostile instructions forward.
Runaway execution and repeated attempts
Test recursive tool use, repeated retries, excessive chain depth, token consumption and cost against limits you have defined for the system. Observe whether a task can continue indefinitely or consume more resources than intended. Include multi-turn paths where relevant. NIST’s January 2025 guidance on evaluating agent hijacking notes that multiple attack attempts can produce a more realistic evaluation than relying on a single attempt, so preserve repeatability and record variation across runs.
Test the enforcement layer independently
Model behavior is not authorization. A prompt test in which the agent refuses a request does not show that the tool endpoint would reject an unauthorized call. OWASP recommends independent validation of tool calls and cautions that prompt-injection examples are smoke tests, not a security benchmark.
Send crafted requests directly to the API gateway, policy service or tool execution layer, bypassing the model where your test environment permits. For each call, verify that execution code independently checks:
- the caller’s identity and whether it is permitted to act;
- the requested scope and target resource;
- the arguments and whether they fall within the authorized task;
- whether the action requires approval, and whether that approval is valid for this exact action.
Apply stronger, action-specific checks to destructive, financial, administrative and externally visible operations. Bind approval to the particular action being approved rather than treating general consent as permission for any subsequent action. If authorization or approval validation is unavailable, the system should fail closed rather than proceed.
Test denial paths as carefully as success paths. Confirm that a blocked call does not still cause a partial change, leak information in an error or retry through another tool, or leave the agent able to reach the same capability by a different route. Record the enforcement point and the observable result for each scenario.
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Agree on pass conditions before testing
For each abuse case, define what must be blocked, what may be allowed, and which actions need human or policy approval. Specify what data may leave the system and what evidence must be logged for investigation. Release criteria should reflect the consequences and authority of the agent, rather than treating every failure as equally severe.
Track legitimate task completion alongside security failures. A control that blocks every tool call may prevent some attacks, but it can also defeat the agent’s purpose. The evaluation should distinguish an effective denial of an unauthorized action from a blanket failure to perform authorized work.
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Make scenarios repeatable
Keep each test case with its starting conditions, input channel, relevant permissions, expected outcome and observed result. Record the tested configuration, including the prompts, tools, memory and retrieval setup, policies, and model provider. Preserve unresolved risks and the rationale for any accepted risk so a later reviewer can understand the release decision.
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Run the suite before production and again after material changes to prompts, tools, memory, retrieval sources, policies or model providers. A change can create a new path or alter the agent’s behavior even if the rest of the system appears unchanged. NIST and OWASP guidance support repeated evaluation rather than treating a one-time prelaunch test as permanent assurance.
How to compare evaluation methods
If you are choosing between internal test methods or evaluating a testing product, compare the coverage and evidence it provides—not just the number of prompts it can run. OWASP and NIST guidance points to several practical dimensions:
| Evaluation dimension | What to verify |
|---|---|
| Coverage | Does it exercise direct and indirect injection, multi-turn behavior, tools, retrieval, memory, exfiltration paths and privilege boundaries? |
| Realism and reproducibility | Can it represent actual workflows, preserve repeatable scenarios and support multiple attack attempts? |
| Enforcement coverage | Does it test only model responses, or can it also assess gateways, policies, API authorization and tool execution? |
| Evidence and operations | Can results be traced to a configuration and specific test cases, with useful logs, clear failure criteria and a workable CI process? |
| Isolation and data handling | Can testing occur without production secrets, with suitable limits on filesystem access and outbound network requests? |
| Maintenance | Can the tests be rerun when models, prompts, policies, tools or data sources change? |
OWASP’s agent-security cheat sheet names promptfoo as an example of an open-source framework for LLM and agent testing, including red-teaming and prompt-injection scenarios. A framework can help run scenarios, but the organization still has to define its own authorized tasks, trust boundaries, enforcement checks and release criteria. Tooling does not by itself establish that an agent’s permissions are correct or that a gateway enforces them.
What a defensible evaluation result looks like
A useful evaluation produces more than a collection of successful refusals. It shows which workflow and configuration were tested, which untrusted inputs were introduced, what the agent attempted, what the execution layer allowed or blocked, and whether data crossed a boundary. It also identifies the remaining risks and the limits of the tested environment.
Before release, confirm that every meaningful capability has an owner, a defined authorization boundary and a testable enforcement point. Confirm that abuse cases cover the agent’s real input channels, not only direct prompts; that high-impact actions have action-specific controls; and that the same suite can be rerun after relevant changes. The release decision should rest on both observed agent behavior and independently verified controls around what it can do.
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