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AI agent runtime security is the set of controls that governs what an agent can actually do while it is running: which identity it uses, which tools and data it can reach, and what actions it may take. A system prompt can ask an agent to behave safely, but it cannot authorize or contain an action. Those decisions must be enforced in the execution path and by the services the agent calls.
What runtime security protects
An AI agent combines a model with an execution system. The model interprets instructions and proposes actions; the surrounding system supplies tools, credentials, data, memory, and connections to external services. Runtime security focuses on the boundaries around that operating system, not just on the model’s responses.
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That means ordinary application and infrastructure security still matters: authenticate callers, authorize access, protect secrets, isolate workloads, and monitor changes. Agent-specific controls address how model behavior and untrusted context can influence the use of those systems. OWASP’s AI Agent Security Cheat Sheet, LLM06:2025 Excessive Agency, and Runtime Application Security Threats guidance address these risks.
A content filter or system prompt may reduce unwanted output, but neither is an access-control boundary. If an agent can delete records, send messages, or read sensitive files, permissions and safeguards must be enforced outside the model.
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How runtime security works
A typical secure action path separates the agent’s proposal from permission to execute it. The following is a useful design pattern, not a claim that every agent platform implements the same architecture.
- Receive input. The agent receives a user request and may also retrieve documents, inspect a website, or receive tool output. Treat external and retrieved content as untrusted, even when it appears to contain instructions.
- Propose an action. The model produces a tool request, such as looking up a record or changing a setting. That request is data to evaluate, not proof that the action is permitted.
- Check the request outside the model. A policy or execution layer verifies the caller and agent identity, the requested tool and operation, argument values, resource scope, and any required approval. It should deny requests that do not satisfy policy.
- Execute with bounded access. If authorized, the tool runs with credentials and permissions limited to the task. Isolation, filesystem restrictions, network rules, and quotas constrain what can happen if the agent or tool behaves unexpectedly.
- Record the outcome. Monitoring captures the identity, request, relevant state changes, and result in a system the agent cannot alter. Sensitive credential values should not be written to logs.
For consequential actions, authorization should be checked at the tool or downstream service boundary as well as in any intermediary policy layer. This prevents a model’s classification—or a compromised intermediary—from becoming the only gate.
Why agents create runtime risks
Excessive functionality, permissions, and autonomy
OWASP describes excessive agency as the risk of damaging actions in response to unexpected, ambiguous, or manipulated model outputs. The common causes are giving an agent too many capabilities, granting permissions broader than its task requires, or allowing it to act with too little oversight.
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For example, a read-only task should not depend on a tool credential that can also modify or delete records. A shared, broadly privileged identity makes it harder to limit or attribute actions. Deletion without independent confirmation gives an error or manipulated instruction an unnecessarily large impact.
Prompt injection through external content
Instructions intended to manipulate the agent can arrive indirectly in documents, websites, tool results, or tool metadata. A prompt filter alone cannot make every such input safe. Label and separate untrusted content, validate tool arguments in code, and check the initiating caller’s permissions independently of the model’s interpretation.
OWASP’s LLM Prompt Injection Prevention Cheat Sheet recommends defense in depth, including action-specific approval for high-risk operations and treating model output as untrusted when it reaches downstream systems.
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Memory and infrastructure weaknesses
Persistent or shared memory can carry a malicious or inaccurate write into a later session or another agent’s context. Runtime threats also include manipulated retrieval data, weak sandbox boundaries, and agent escape. These are security and infrastructure concerns as well as model-behavior concerns.
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Give each task a limited identity
- Use a distinct identity for each agent or workload so activity can be attributed and access can be revoked without disrupting unrelated systems.
- Prefer short-lived, task-scoped credentials. Avoid placing long-lived or production secrets in prompts or in locations the agent can read.
- Bind delegated actions to the initiating user and that user’s authorization scope. An agent should not inherit broader rights merely because it runs under a service identity.
Authorize every tool action
- Start from deny and explicitly allow only the tools and operations needed for the task.
- Use narrow argument schemas and validate arguments in execution code. Check both the requested operation and the specific resource it targets.
- Separate read and write credentials where practical. A read permission should not silently enable a write path.
- For destructive, financial, administrative, or externally visible actions, bind approval to the exact action and parameters. Use short-lived authorization and replay protection where appropriate.
- Fail closed if policy or approval cannot be validated. A model’s claim that an action is safe does not grant permission.
Contain execution and cap resource use
Choose an OS-level sandbox, container, microVM, or disposable environment appropriate to the threat model. Restrict filesystem access and network egress, avoid mounting sensitive personal directories, and keep production credentials outside the execution environment where possible.
Check the actual coverage of each boundary. A shell sandbox may not restrict a separate file tool, an MCP server, or a service reachable through an allowed API. Sandboxing limits exposure; it does not make every permitted connection safe.
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Enforce limits at the platform or service layer for CPU, memory, API volume, tool invocations, retries, recursion, and wall-clock time. Do not rely on the agent to stop itself when it reaches a sensible limit.
Protect memory and context
- Treat retrieved documents, vector-store results, and shared memory as untrusted input.
- Protect stored data in transit and at rest, and track provenance such as source, writer, timestamp, and partition.
- Validate entries before adding them to active context, and restrict which agents can read or write shared memory.
- Keep append-only records of important state changes where appropriate.
Provenance and integrity checks have operational costs, including additional storage and latency. Separating malicious updates from legitimate ones at scale remains difficult, so memory controls should complement—not replace—authorization and isolation.
Monitor activity outside the agent’s control
Log tool calls, commands, file writes, network requests, identity, initiating user, session, and outcome. Keep secret values out of logs and send records to a system the agent cannot modify. Useful alerts include unexpected network destinations, credential-file access, bulk reads, newly introduced MCP servers, and sensitive configuration changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to choose and validate an implementation
Runtime security may be enforced at several points: in an agent framework, an MCP gateway, a policy engine, an operating-system sandbox, or the downstream service. OWASP’s AI Agent and MCP Security guideline names examples across policy, mediation, testing, and sandboxing categories; those examples are not comparative product assessments. Choose controls by checking the boundaries they actually cover.
- Enforcement point: Identify where requests are allowed or denied, and whether downstream services independently authorize access.
- Scope: Confirm whether identity and permissions can be limited per user, agent, task, tool, and resource.
- Coverage: Check which surfaces are controlled: shell, files, network, APIs, MCP servers, memory, and inter-agent messages.
- High-risk actions: Verify exact-action approval, denial behavior, and fail-closed handling.
- Operations: Assess attributable logging, resource limits, version pinning, latency, compatibility, and the work required to maintain policies and tests.
- Residual exposure: Consider what an agent can still do through services and APIs that remain permitted.
Test the real boundaries
- Test direct and indirect prompt injection at the channels the agent uses, including retrieved content and tool output.
- Use harmless data and instrumented substitutes for tools so tests cannot cause real deletion, disclosure, or external actions.
- Check that unauthorized tools, out-of-scope resources, malformed arguments, failed approvals, and policy errors are denied.
- Verify that logs capture the attempted action and outcome without exposing secrets.
- Retest after changes to prompts, tools, memory, retrieval, providers, or permissions.
A small sample test is a smoke test, not a security benchmark. No single safeguard is sufficient: sandboxes can have gaps, permitted APIs can still enable data exfiltration, and frequent approval requests can lead to approval fatigue. The relevant test is whether the deployed system enforces its intended boundaries under realistic, adversarial conditions.
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