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This design applies whether the agent reads web pages, email, documents, tickets, tool results, or conversation history. External text is data to analyze; it must never be allowed to redefine policy, expand permissions, or bypass the code that executes actions.
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What prompt injection and unauthorized action look like
A prompt injection is content that attempts to change an agent’s behavior by posing as an instruction. A direct injection comes from the person using the agent, such as a request to ignore its safety rules. An indirect injection is embedded in content the agent is asked to process: a web page, a document, an email, a calendar entry, a retrieved knowledge-base passage, a tool result, shared memory, or a message from another agent.
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The dangerous step is not merely producing a bad sentence. It is allowing model output to trigger a side effect without an independent decision that the caller, operation, target, arguments, and scope are authorized. An injected instruction might cause data exfiltration, an unauthorized message, a destructive change, privilege escalation, memory poisoning, or an expensive loop.
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OWASP’s agent-security material lists prompt injection alongside tool abuse, data exfiltration, memory poisoning, goal hijacking, excessive autonomy, cascading failures, supply-chain attacks, and unbounded cost. These risks overlap: a malicious document can hijack a goal, induce a tool call, and use the resulting access to disclose private data.
Why labels and system prompts are not a boundary
Marking text as “untrusted” or placing a warning in a system prompt helps the model interpret context, but it does not enforce a security boundary. The same model processes both trusted instructions and hostile content, and an attacker can exploit ambiguity, authority-looking language, encoded text, or a long conversation history. A final response that sounds safe also does not prove that an unauthorized tool call did not already occur.
Map every trust boundary before granting capabilities
Start with an inventory of everything that can enter the agent context and everything the agent can influence. Draw a boundary around the model and treat each crossing as a security-relevant event.
- Human inputs: user messages, uploaded files, support tickets, form fields, and instructions copied from other systems.
- Retrieved content: search results, web pages, documents, code repositories, vector-store passages, and database rows.
- Operational context: conversation history, summaries, long-term memory, scratchpads, and plans persisted between runs.
- Tool interfaces: tool descriptions, schemas, parameters, return values, error messages, and output from external APIs.
- Agent-to-agent traffic: messages from sub-agents, workflow nodes, plugins, or automation services.
- Secrets and identities: API keys, session tokens, service accounts, delegated user credentials, and signing keys.
- Side effects: database writes, email or chat messages, file changes, payments, account changes, deployments, and network requests.
For each path, document whether content is trusted, partially trusted, or untrusted; whether it can influence planning; and which component validates a resulting action. Do not let a tool’s natural-language description become a permission grant. The description explains how to call a capability; trusted code must decide whether this particular caller may call it.
A boundary map that exposes common failure modes
| Boundary | Typical attack | Required control |
|---|---|---|
| User to agent | A user asks the agent to reveal another person’s data or override policy. | Authenticate the user and authorize the requested operation independently of the model. |
| External content to context | A web page or email says to upload secrets or change the agent’s goal. | Keep content in a data channel, mark provenance, and prevent it from changing policy or permissions. |
| Model to tool | The model invents a target, broadens arguments, or selects a dangerous operation. | Use a narrow tool allowlist, strict schemas, argument validation, and policy checks. |
| Tool to downstream service | A service trusts an agent’s claim that an action was approved. | Re-authenticate and authorize at the downstream service for every request. |
| Memory to future runs | Injected text is saved as a durable instruction. | Validate provenance, isolate memory by user and task, and require review before promoting content to policy. |
Make the agent’s authority as small as the task allows
The safest capability is one the task does not need. Apply least agency: “give an agent only the autonomy, tools, and access its task requires, for only as long as it needs them,” as stated in the OWASP DevSecOps guidance on AI Agent and MCP Security.
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Separate read and write capabilities
Expose a function that reads messages rather than a broad mail extension that can also send, delete, or change forwarding rules. Separate database queries from mutations. Use distinct functions for previewing a deployment and applying it. If a task only needs lookup, issue a read-only identity and reject write operations at the credential or service layer.
Use separate identities and scoped credentials
- Give each agent or workflow a dedicated service identity instead of a developer’s or administrator’s personal account.
- Scope tokens to specific resources, operations, tenants, and environments.
- Prefer short-lived credentials issued for one task or run.
- Keep production credentials separate from development and test credentials.
- Do not place secrets in prompts, retrieved documents, tool results, or model-visible memory.
- Rotate and revoke credentials independently so one compromised agent does not expose unrelated systems.
When user delegation is required, preserve the user’s identity and authorization context through the call chain. A model-generated statement such as “the user approved this” is not evidence of approval.
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Use deterministic workflow logic whenever AI is not needed. If model output influences tool selection, constrain it to an allowlist of operations and validate the selected operation in code. An allowlist limits the capability surface; it does not replace authentication, authorization, argument checks, or downstream enforcement.
Enforce authorization outside the model
Every side effect needs complete mediation: the execution component checks the request each time rather than trusting a previous model decision or an earlier check. Validate five properties before invoking a tool:
- Acting identity: Which authenticated user, service, or agent is making the request?
- Operation: What exact action is requested—read, create, update, delete, send, transfer, or deploy?
- Target: Which account, record, file, environment, recipient, or URL will be affected?
- Arguments: Are values present, correctly typed, within allowed ranges, and free of injection into the destination system?
- Scope: Does the identity have permission for this operation on this target in this tenant and environment?
Perform the check in trusted code or a policy service immediately before execution. Recheck at the downstream API, database, or infrastructure control plane. This protects against time-of-check/time-of-use changes and against a tool wrapper that accidentally widens access.
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Validate outputs for their destination
Model output is untrusted data when passed to another system. Use typed schemas, length and range limits, canonicalization, and destination-specific escaping. Parameterized database queries are required for database access; filtering a few SQL keywords is not an adequate defense. For URLs, validate the scheme, hostname, port, redirect behavior, and network destination. For file operations, resolve paths safely and enforce an allowed directory. For messages, validate recipients, attachments, and content classification before sending.
Example execution gate
request = model_proposed_call # untrusted data
identity = authenticate_context()
operation = schema_validate(request.operation)
target = canonicalize_target(request.target)
args = validate_arguments(operation, request.arguments)
policy.require(identity, operation, target, args)
if operation.is_high_impact:
approval.require_exact(identity, operation, target, args)
downstream.authorize_again(identity, operation, target, args)
execute(operation, target, args)
The model may propose the call, but it cannot skip any line in this gate. If the target or arguments change after approval, treat the request as a new operation and obtain a new decision.
Require meaningful approval for consequential actions
Pause actions that are destructive, financial, administrative, externally visible, or difficult to reverse. Examples include deleting records, sending messages to customers, changing access rights, moving money, publishing content, deploying code, rotating credentials, and shutting down infrastructure.
What a reviewer must see
- The authenticated actor and the agent or workflow requesting the action.
- The exact operation and the concrete target resource.
- All material arguments, recipients, amounts, attachments, and affected environments.
- The reason or user request that initiated the action, with untrusted content clearly identified.
- Whether the action is reversible, its expected impact, and its expiry or timeout.
Approval must be enforced at the execution boundary, not displayed only in the user interface. Bind the approval to a canonical representation of the operation, target, and arguments. If any material detail changes, invalidate the approval. For critical operations, use stronger authentication, an independent approver, and replay protection such as a one-time approval identifier and an expiry time.
Design untrusted-content handling deliberately
Keep external material separate from policy and task instructions in your data model and prompts. Preserve provenance so an operator can see which source supplied a statement. Quote or summarize content for the model when full raw access is unnecessary, and remove secrets before insertion into context. Do not automatically copy retrieved instructions into system messages, tool descriptions, memory, or workflow configuration.
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Web pages, email, and documents
Fetch content through a constrained service. Limit network destinations, redirects, file types, size, and processing time. Parse attachments in an isolated environment. Treat instructions such as “ignore previous rules,” “send this file,” or “call this endpoint” as claims in the document, not as commands. The agent can report them to the user or security system, but only trusted application logic can authorize an action.
Tool results and error messages
Tool output can contain attacker-controlled strings even when the tool itself is trusted. Return structured fields with explicit provenance, truncate unexpected content, and prevent an error message from altering available tools or policy. A tool that retrieves a ticket should not be able to redefine the agent’s role through the ticket text.
Memory and conversation history
Partition memory by tenant, user, and purpose. Store source, timestamp, and confidence with each item. Do not treat a model-generated summary as a trusted policy. Require an explicit, authorized process to promote information into durable preferences or workflow rules, and provide deletion and quarantine mechanisms when poisoning is suspected.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test direct and indirect injection at the real boundary
Security tests must exercise the same channel and tool boundary used in production. A prompt typed into a test console is not an adequate substitute for an instruction embedded in a retrieved document or tool result.
Build a repeatable test plan
- Define the protected assets, allowed actions, prohibited actions, and expected authorization decisions.
- Create direct injection cases that ask the agent to ignore policy, reveal secrets, broaden scope, or perform an unapproved action.
- Place equivalent instructions in realistic external channels: a test web page, document, email, ticket, memory item, or tool response.
- Use dummy credentials, synthetic records, sandbox accounts, and instrumented tool substitutes.
- Record the proposed call, validated identity, policy result, approval result, downstream response, state change, and any attempted disclosure.
- Fail the test if a prohibited side effect occurs, even when the final natural-language answer refuses the request.
- Repeat tests after changing models, prompts, tools, retrieval components, policies, or credential scopes.
OWASP describes example injection inputs as smoke tests rather than a representative benchmark. Treat them as starting cases and add cases specific to your data formats, languages, tools, tenants, and failure modes. Test multi-step attacks in which an apparently harmless read enables a later write or disclosure.
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Evidence that matters
| Evidence | What it demonstrates | What it does not demonstrate |
|---|---|---|
| Final refusal text | The model declined in that conversation. | That no tool call or side effect happened earlier. |
| Blocked tool call | The execution gate rejected a request. | That another path to the same downstream action is protected. |
| No state change in a sandbox | The tested path did not alter instrumented state. | That production credentials or untested tools are safe. |
| Authorization logs | Identity, operation, target, and policy decisions were recorded. | That the policy itself expresses the right business rule. |
Monitor, rate-limit, and contain failures
Log enough detail to reconstruct an incident without storing unnecessary secrets. Useful fields include run and conversation identifiers, tenant, authenticated actor, model and tool versions, input provenance, proposed operation, canonical target, policy decision, approval identifier, downstream response, latency, token or cost data, and resulting state change.
Detection signals
- Repeated denied calls or rapid changes in requested targets.
- A sudden switch from read operations to writes, exports, or credential-related actions.
- Attempts to access resources outside the user’s normal tenant or project.
- Large or unusual outbound data, repeated retries, or loops.
- Tool calls that do not match the declared task or workflow state.
- Changes to memory, prompts, tool schemas, or policies outside approved deployment paths.
Rate-limit risky operations by identity, tenant, tool, target, and time window. Add circuit breakers for repeated failures, unusual volume, or policy violations. For high-risk systems, provide an emergency shutdown capability that can disable tools or revoke credentials without requiring the model to cooperate.
Incident response and recovery
- Stop further side effects by disabling the affected tool, workflow, identity, or network route.
- Revoke and rotate credentials that may have been exposed.
- Preserve logs, tool payloads, approvals, and state snapshots for investigation.
- Identify affected records, recipients, environments, and tenants.
- Contain or reverse changes using tested recovery procedures.
- Quarantine poisoned memory, documents, prompts, or tool definitions.
- Patch the policy or boundary, add a regression test, and restore capability gradually.
- Notify affected users and regulators when required by your incident obligations.
AWS guidance for agentic AI also emphasizes observability, emergency shutdown capabilities, recovery, and continuity planning for critical operations. Design these controls before deployment rather than improvising them during an outage.
Compare implementation designs by security properties
| Design choice | Lower-risk characteristic | Warning sign |
|---|---|---|
| Authority | Task-specific tools, resources, and read/write permissions with short-lived credentials. | One general-purpose agent has administrator access across systems. |
| Execution boundary | Deterministic code and downstream services authorize every operation. | The model’s plan or text assertion is treated as authorization. |
| Consequence | High-impact actions are reversible, staged, or blocked pending review. | Destructive or financial operations execute immediately. |
| Approval quality | A reviewer sees and approves the exact target and arguments at execution time. | A generic “approve agent” switch authorizes future unspecified actions. |
| Test evidence | Direct and indirect injections are tested through real channels with observed side effects. | Only prompt examples and final answers are evaluated. |
| Operational containment | Detailed logs, rate limits, revocation, shutdown, recovery, and continuity procedures exist. | There is no way to stop a runaway agent without taking down unrelated systems. |
A practical rollout sequence
- Start read-only: deploy retrieval and analysis with no write-capable tools, and verify tenant and resource isolation.
- Instrument the path: log proposed calls, policy decisions, approvals, downstream responses, and state changes before enabling side effects.
- Implement complete mediation: add identity, operation, target, argument, and scope checks in the executor and downstream services.
- Split capabilities: replace broad tools with narrow functions and separate read and write identities.
- Add approval gates: require exact, expiring approval for destructive, financial, administrative, or externally visible actions.
- Exercise attacks: test direct prompts and malicious content in every external channel, using sandboxed state.
- Enable containment: configure rate limits, anomaly alerts, credential revocation, emergency shutdown, and recovery drills.
- Expand gradually: grant additional tools only when tests and operational evidence show that the new capability is necessary and bounded.
What not to rely on
- Prompt filtering alone: attackers can vary wording, encode instructions, or place them in indirect content.
- A stronger system prompt alone: model behavior is probabilistic and does not enforce authorization.
- Content labels alone: “untrusted” text can still influence a model unless execution code rejects unauthorized effects.
- One global allowlist: a permitted tool can still be misused with an unauthorized target or argument.
- Human approval without detail: approving an agent generally is not approval of a specific operation.
- Final-answer review: a safe-sounding response cannot undo an earlier side effect.
- Post-incident logs only: if critical events are not recorded at execution time, reconstruction may be impossible.
Governance and ownership
Assign an owner for each tool, data source, policy, credential, and emergency control. Define who can add a tool, change its schema, widen a scope, approve a high-impact operation, revoke an identity, and restore service after containment. Review these assignments whenever an agent gains a new integration or crosses a trust boundary.
OWASP reports collaboration from more than 100 industry experts, researchers, and practitioners behind its 2026 Top 10 for Agentic Applications. That number describes contributors, not attack frequency or control effectiveness. There is no general published figure in the cited guidance that establishes a universal prompt-injection-defense success rate or a rate of unauthorized agent actions; measure your own deployment with repeatable, side-effect-aware tests instead.
Quick Recap
Security checklist
- All external content is treated as data, with provenance and isolation from policy.
- Each agent has only the tools, resources, operations, and credential lifetime its task needs.
- Read and write capabilities use separate identities where practical.
- Every tool call is authorized in trusted code and rechecked downstream.
- Arguments and destinations are schema-validated and safe for their specific sink.
- High-impact actions require approval for the exact operation and parameters.
- Direct and indirect injection tests run through production-like channels in a sandbox.
- Logs capture proposed calls, decisions, approvals, responses, and state changes.
- Rate limits, anomaly detection, credential revocation, emergency shutdown, and recovery procedures are tested.
- Policy, tool, prompt, memory, and model changes trigger regression testing and review.
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