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Retrieved content is an untrusted input, not an instruction source. A webpage, uploaded file, search result, support ticket, or tool response can contain text that tries to redirect an AI feature, expose data, or trigger an unauthorized action. You cannot guarantee prevention with a clever system prompt or one filter. Reduce the likelihood and blast radius by enforcing permissions before retrieval, isolating and screening context, limiting model capabilities, requiring approval for sensitive actions, and monitoring the complete retrieval-to-action path.
What indirect prompt injection is
In a direct prompt injection, the attacker puts the malicious instruction in the request sent to your model. In an indirect prompt injection, the attacker places it in content your application later retrieves or asks the model to read. Examples include a public webpage, a poisoned knowledge-base article, a customer-uploaded PDF, an email, a search snippet, or data returned by another tool.
When that content is inserted into the model’s context, instruction-like text can compete with the instructions you intended to control the application. OWASP describes attacker-controlled websites and files as delivery channels and warns that the resulting behavior can manipulate users or connected systems. See OWASP’s LLM01: Prompt Injection guidance.
The security boundary is therefore not just the chat box. It runs through every stage that carries external content toward a model and every action the model can request afterward:
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- Ingestion: crawling pages, parsing files, receiving uploads, or accepting tool responses.
- Retrieval: selecting chunks from an index, database, search engine, or memory store.
- Context construction: combining system instructions, user input, retrieved data, conversation history, and tool results.
- Inference: generating an answer or a proposed plan.
- Action: calling APIs, changing records, sending messages, deleting data, or modifying access.
Any stage can carry malicious material, and the most serious outcomes occur when a model-influenced decision reaches a privileged tool without an independent authorization check.
Why a system prompt or delimiter cannot be your security boundary
Your context should clearly tell the model that retrieved passages are data only. Delimiters, labels such as UNTRUSTED_CONTENT, and an instruction not to follow commands found in a passage improve the model’s chances of treating the material correctly. They do not create a deterministic isolation boundary.
Models can misinterpret novel, obfuscated, multilingual, or indirectly phrased instructions. A malicious passage may also exploit the surrounding task rather than contain an obvious command. OWASP’s prevention guidance states: “Consequently, there is no fool-proof prevention within the LLM, but the following measures can mitigate the impact of prompt injections.” Read the OWASP LLM Prompt Injection Prevention Cheat Sheet.
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Map the complete trust boundary before changing prompts
Start with a data-flow inventory. For each input, record where it originated, who can modify it, how it is authenticated, what permissions apply, and whether it can influence a tool call or user-visible decision.
- Web pages and syndicated feeds, including hidden text, metadata, alt text, comments, and rendered content.
- Files uploaded by users or received from partners, including PDFs, office documents, images processed with OCR, and archives.
- Search results, vector-store chunks, document summaries, and cached answers.
- Email, tickets, chat transcripts, calendar data, and records imported from business systems.
- Responses from browsing, code, database, or HTTP tools.
- Long-term memory or saved user preferences that can be edited through another path.
- Conversation history supplied by a client rather than generated and stored by your trusted service.
Preserve provenance for every retrieved item: a stable source identifier, tenant or owner, retrieval time, document version, and the permission decision that allowed it. Provenance lets you explain why a passage appeared, revoke a poisoned source, and connect an answer or action to the data that influenced it.
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Authorize retrieval before the model sees a document
Security trimming belongs in the retrieval layer, not in a later prompt. For each candidate document or chunk, evaluate the requesting user’s identity, tenant, role, resource permissions, and any row- or field-level policy. Do not retrieve a document merely because it is semantically relevant.
A prompt cannot repair an authorization failure: once unauthorized text is in the context, the model may quote it, summarize it, or use it to guide a tool. Apply the same policy to lexical search, vector search, reranking, summaries, cached results, and fallback indexes. Test that a user who loses access cannot continue to receive the content from a cache or a previously generated answer.
Keep the authorization decision and source reference with each chunk passed to the model. If your application cannot explain which permission allowed a chunk, treat that retrieval path as incomplete. The OWASP RAG Security Cheat Sheet covers retrieval controls, bounded context, scanning, and adversarial testing.
Inspect and constrain retrieved context
Normalize before inspection
Convert content into a representation your scanners and policy code can consistently inspect. Decode obvious encodings, extract text from supported file formats, remove irrelevant markup, and retain a link to the original bytes for investigation. Keep the raw source quarantined when parsing fails; do not silently pass an unparsed file to a model with broad capabilities.
Look for instruction-like and concealment patterns
Flag text that addresses the assistant, attempts to override prior rules, requests secrets, asks for tool use, or tells the model to hide its behavior. Also flag suspicious formatting, invisible or extremely small text, encoded payloads, prompt fragments split across chunks, and content that directs the model to ignore provenance. Pattern checks are useful triage, not proof of safety: attackers can vary wording and scanners can produce false positives.
Bound the context
Retrieve only the passages needed for the user’s task. Limit the number and size of chunks, reject irrelevant or duplicate material, and avoid placing an entire page or mailbox in the context when a few attributed excerpts are sufficient. Smaller, purpose-specific contexts reduce the number of places an injection can hide and make review and logging more practical.
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Mark data and preserve attribution
Represent each passage as structured data in your application, with fields such as source ID, title, owner, retrieval time, and text. When serializing it into a prompt, use an unmistakable boundary and state that the text is untrusted reference material whose instructions must not be followed. Show citations or source IDs in the answer where appropriate so users and investigators can trace claims.
Microsoft’s RAG prompt-engineering guidance recommends explicitly treating context as data, screening retrieved chunks, flagging instruction-like content, and monitoring prompts and outputs. These are defense-in-depth recommendations, not a guarantee that a particular service removes the risk: Microsoft’s RAG prompt-engineering guidance.
Use a constrained context contract
A practical context contract separates trusted instructions from untrusted data and makes the expected behavior explicit. For example, your application can assemble a request with these logical sections:
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- Trusted policy: the task, safety rules, output schema, and tool restrictions authored by your application.
- User request: the authenticated user’s current question, after input validation and abuse controls.
- Untrusted references: attributed passages labeled as data only, enclosed in a distinct structure.
- Available actions: narrowly defined tools and the conditions under which the model may propose them.
Do not place retrieved text in a field that your orchestration code interprets as a system instruction. Do not concatenate untrusted strings into tool names, API paths, SQL fragments, policy expressions, or executable code. Use typed fields and schemas so an output such as send_message is a proposed operation that your server validates, not an instruction that is executed because it appeared in free text.
Prompt instructions should tell the model to treat references as non-authoritative, ignore commands found in them, preserve uncertainty, and identify suspicious content rather than obeying it. Keep those instructions short and testable. Their purpose is to improve model behavior, while deterministic checks enforce the actual security policy.
Constrain what the AI can retrieve and do
Least-privilege data access
Give each feature access only to the collections, fields, and tenants required for its task. Separate read-only retrieval identities from identities that can modify data. Avoid a shared service account whose permissions span unrelated systems.
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Scoped tools and identities
Expose narrow operations rather than a general-purpose shell, unrestricted HTTP client, or arbitrary database query. Validate arguments against an allowlist and an explicit schema. Scope credentials to the current user, tenant, resource, and operation. Where supported, use short-lived privileges so a stolen or misused capability expires quickly.
Independent authorization at the action layer
Every tool invocation must re-check authorization on the server that owns the resource. Do not rely on the model’s statement that a user is allowed to perform an action, and do not trust a permission value copied from retrieved content. Enforce rate limits, transaction limits, destination allowlists, and data-loss policies in code.
Separate planning from execution
Have the model produce a plan or structured proposal first. A policy engine can then validate the proposed operation, affected resources, and parameters before an executor receives it. This separation makes it easier to detect plan drift: if the model changes the target, scope, or operation after retrieval, stop the flow instead of silently continuing.
Require human approval for high-impact actions
Insert an approval gate before actions that send external communications, delete or overwrite data, purchase goods or services, change permissions, publish content, transfer funds, or reveal sensitive information. Show the approver the exact proposed action, target, parameters, user identity, and relevant source citations. Do not ask for approval to a vague summary such as “continue with the task.”
Approval should be bound to the specific operation and expire if the plan changes. Record who approved it, when, and what was executed. For low-risk, reversible actions, you may use automated policy thresholds; for privileged or irreversible actions, require an explicit human decision. OWASP and Microsoft both recommend human review for risky operations as part of a layered design.
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Monitor the retrieval-to-action path
Logging only the final answer hides the evidence needed to investigate an injection. Subject to privacy and retention requirements, capture:
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- The authenticated user, tenant, session, and feature being used.
- Document and chunk IDs considered, retrieved, filtered, and passed to the model.
- Permission results and provenance metadata for each accepted source.
- Normalized or flagged instruction-like content and scanner decisions.
- Model input and output versions, safety classifications, and structured plans.
- Tool calls, arguments, authorization results, approvals, execution outcomes, and errors.
Alert on unusual patterns: a retrieval request crossing tenants, repeated attempts to access denied sources, sudden requests for secrets, tool calls outside the feature’s normal scope, changed destinations, or a plan that drifts after a new document is retrieved. Protect logs because they may contain the same sensitive material the model handled.
Microsoft’s indirect-injection guidance describes defense-in-depth measures including prompt shields, data marking or spotlighting, plan-drift monitoring, tool-chain analysis, least privilege, short-lived privileges, and human review. The page’s search listing gives a last-updated date of March 23, 2026; that date identifies a page revision, not a measured effectiveness result. See Microsoft’s guidance on defending against indirect prompt injection.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test the full flow with adversarial content
Unit-testing a prompt is not enough. Build tests that start with an attacker-controlled page or file and follow the content through authorization, indexing, retrieval, context construction, model output, tool selection, approval, and execution.
- Poisoned retrieval: a relevant document contains an instruction to ignore policy, reveal secrets, or call a tool.
- Access-control bypass: a chunk from another tenant is highly relevant but must never be returned.
- Hidden or obfuscated instructions: text uses markup, encoding, unusual whitespace, another language, or split phrases.
- Attribution tampering: a passage tells the model to cite a different source or conceal where a claim came from.
- Unauthorized tool invocation: retrieved text attempts to trigger sending, deletion, purchase, access changes, or data export.
- Plan drift: the model changes a previously approved target or operation after seeing a new passage.
- Parser and cache failures: malformed files, stale permissions, duplicate chunks, or cached answers bypass normal checks.
Define expected outcomes for each case: block the retrieval, quarantine the source, return a safe refusal, require approval, or allow a read-only answer with a warning. Re-run the suite after changing models, prompts, parsers, indexes, permission code, tools, or policies. OWASP’s 2025 material emphasizes that neither RAG nor fine-tuning fully mitigates prompt injection and recommends clearly separating and denoting untrusted content: OWASP LLM01:2025 Prompt Injection.
Compare controls by where they enforce policy
| Control location | Enforcement style | Impact boundary | Traceability | Operational burden |
|---|---|---|---|---|
| Retrieval and index | Deterministic authorization, tenant filtering, provenance checks | Prevents unauthorized or irrelevant data from entering context | Can link each chunk to a source and permission decision | Requires synchronized permissions, cache invalidation, and index maintenance |
| Context preparation | Normalization, scanning, labeling, bounded selection | Reduces exposure to suspicious or excessive content | Preserves chunk IDs and screening decisions | Must handle false positives, parser gaps, and new attack forms |
| Model or service filter | Probabilistic detection and instruction-following guidance | May reduce unsafe interpretations but cannot guarantee isolation | Depends on available model and safety telemetry | Requires tuning, monitoring, and retesting after model changes |
| Tool and action layer | Deterministic schemas, authorization, allowlists, rate limits | Contains damage even if the model follows malicious text | Records proposed and executed operations separately | Needs policy code for each operation and careful error handling |
| Monitoring and response | Detection, alerting, approval, quarantine, revocation | Limits duration and downstream effects of an evasion | Connects inputs, sources, plans, calls, approvals, and outcomes | Requires on-call ownership, privacy controls, and incident procedures |
Use the layers together. A scanner is not a substitute for authorization; a permission check is not a substitute for tool restrictions; and a human approval step is not a substitute for investigating poisoned content.
Contain an attack when a layer fails
Assume that some malicious material will evade detection. Build a stop path that operators can use without waiting for a model response:
- Disable or revoke the affected tool identity and pause automated execution.
- Quarantine the source document, URL, chunk, or index version that carried the suspected injection.
- Invalidate cached retrievals and generated answers derived from the quarantined source.
- Use provenance and logs to identify users, tool calls, records, and external recipients affected.
- Reverse or correct downstream changes where possible, and notify responsible owners when an external action occurred.
- Patch the parser, retrieval policy, context builder, tool authorization, or approval rule that failed.
- Add a regression case that reproduces the attack and rerun the complete adversarial suite.
Keep read-only answering separate from write-capable workflows so that disabling actions does not require taking the entire feature offline. Design reversible operations and transaction limits wherever the business process permits.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsA practical implementation sequence
Teams can reduce risk in the following order, starting with controls that prevent the largest impact:
- Inventory inputs and actions. Draw the path from every external source to every model and tool, and identify the data and operations reachable at each step.
- Fix retrieval authorization. Enforce user and tenant permissions before ranking or returning chunks; remove insecure caches and preserve provenance.
- Reduce context. Retrieve only task-relevant passages, normalize them, scan for suspicious content, and label them as untrusted data.
- Harden orchestration. Keep trusted instructions separate, use typed outputs, and prevent untrusted strings from becoming tool names, code, queries, or destinations.
- Reduce privileges. Split read and write identities, narrow tool schemas, use short-lived credentials where available, and apply independent server-side authorization.
- Add approvals. Gate sensitive, irreversible, or externally visible actions with an operation-specific review screen.
- Instrument and test. Log provenance, decisions, outputs, and calls; run indirect-injection tests and monitor plan drift.
- Prepare response. Provide revocation, quarantine, cache invalidation, investigation, rollback, and regression-test procedures.
Common mistakes to avoid
- “The system prompt says ignore instructions in documents.” Helpful guidance is not a hard boundary; enforce policy in retrieval and action code.
- “The content came from our own index, so it is trusted.” An index can contain an attacker-edited page, compromised account, stale permission, or poisoned import.
- “We scan for a few phrases.” Pattern matching misses novel and disguised attacks and can block benign technical text.
- “The model can decide whether the user is allowed.” Authorization must be evaluated by the resource-owning service, not inferred from prose.
- “We only need to log the final answer.” Without retrieved IDs, permission results, plans, and tool arguments, incident response becomes guesswork.
- “Approval means the workflow is safe.” Reviewers need the exact operation and sources; approval must expire when the plan or target changes.
- “RAG or fine-tuning solves prompt injection.” OWASP’s LLM01:2025 guidance explicitly says these techniques do not fully mitigate the issue.
What success looks like
A defensible website AI feature can answer four questions for every sensitive result: What external content influenced this response? Was the requesting user authorized to see it? What exact action did the model propose? Which independent policy check and, when required, which person approved execution? If your system can answer those questions, limit the model’s reachable data and tools, and stop or revoke access when behavior changes, you have moved from prompt hope to layered risk reduction and impact containment.
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