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What indirect prompt injection is—and why search systems are exposed
Prompt injection is a vulnerability in which an attacker manipulates a language model through crafted input. In an indirect attack, the attacker does not need to type into your chat box. They place instructions in a web page, uploaded file, email, ticket, knowledge-base article, image, tool result, or other source that your application later retrieves. The model may parse those instructions as if they were part of its task. Text can be hidden with styling, encoded, split across chunks, or phrased without familiar words such as “ignore previous instructions.”
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Possible outcomes include disclosure of information from another source, misleading answers, unauthorized function calls, arbitrary commands in connected systems, and influence over consequential decisions. RAG and fine-tuning can improve relevance, but neither removes the vulnerability. OWASP describes RAG as redistributing risk across ingestion, storage, retrieval, generation, validation, and agent integration rather than eliminating it.
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The practical security rule is simple: retrieved content can describe facts, but it can never grant itself authority. Authority comes from your application’s identity, authorization policy, and explicitly approved actions.
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Model the trust transitions before choosing controls
Draw every point at which data crosses from one trust domain to another. A useful threat model for an LLM search product looks like this:
| Stage | What crosses the boundary | Primary security question |
|---|---|---|
| Source to ingestion | Web pages, files, email, tickets, APIs, and user uploads | Who supplied this content, when, and has its integrity or format been verified? |
| Document to chunk and embedding | Parsed text, metadata, images, and vectors | Can malicious or hidden content survive parsing, and is provenance retained? |
| Index to retrieval | Candidate chunks returned for a user or agent | Is every result authorized for this caller and tenant before it is selected? |
| Retrieval to context | Chunks, citations, summaries, and conversation state | Are instructions and data separated, bounded, and labeled as untrusted? |
| Model to tool | Tool name, arguments, and proposed plan | Does application code independently authorize this exact action? |
| Model to downstream system | Text, HTML, JSON, SQL fragments, or workflow updates | Are output values validated and safely handled as untrusted input? |
Apply a control at each transition. A refusal generated after an unauthorized chunk has already entered the context is not an access-control fix, and a detector that misses an attack must not leave a powerful tool available without additional restrictions.
Reference architecture for a resistant system
A robust design uses defense in depth. The model is one component in a policy-enforcing pipeline, not the security boundary.
- Ingestion gateway: authenticate sources, record provenance, check integrity and format, and safely process active or hidden markup.
- Authorization-aware indexer: attach tenant, owner, classification, and policy metadata to every chunk and embedding.
- Policy-enforcing retriever: apply the caller’s identity and metadata filters before ranking or returning results.
- Context assembler: keep system and developer instructions separate from delimited, bounded retrieved data.
- Content screening layer: use deterministic checks and probabilistic detectors as additional signals, never as the sole gate.
- Constrained model runtime: expose only the tools and context required for the current task.
- Action policy service: validate tool names and arguments, recheck permissions at execution time, and obtain approval for high-risk actions.
- Output gateway: validate schemas and values, escape or sanitize rendering, and use safe database and API interfaces.
- Observability and response: log policy decisions, detect anomalies, and provide a way to quarantine sources or revoke tools quickly.
For especially powerful workflows, separate components by privilege. A quarantined worker can read and summarize untrusted content without any tool access. A different service, operating under a user identity and explicit policy, can perform approved actions. This reduces blast radius but adds latency, integration complexity, and another boundary that must be tested.
1. Establish provenance and control ingestion
Record who supplied each piece of content
For every source and document, store a stable source identifier, owning account or tenant, ingestion timestamp, content type, parser version, integrity information, and the policy that permitted ingestion. Preserve this metadata when a document is split into chunks and when embeddings are regenerated. Citations should point back to the original source so an investigator can reproduce what the model saw.
Validate and normalize before indexing
- Allow only expected file types and enforce size, decompression, and parser limits.
- Strip scripts and safely handle HTML, macros, active content, and invisible text. Do not execute document content during parsing.
- Detect malformed, encoded, or unusually structured content and route it to quarantine or additional review.
- Maintain a protected baseline or digest for sources whose content should not change unexpectedly.
- Apply a freshness policy: stale material can be excluded, marked, or revalidated rather than treated as current authority.
Do not treat a trusted publisher as a permanent exemption. Accounts are compromised, websites change, and a legitimate document can contain copied attacker text. Provenance helps you investigate and apply policy; it does not make the content authoritative.
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2. Preserve authorization through indexing and retrieval
Carry permissions to every chunk
Attach document-level access rules, tenant identifiers, classifications, ownership, and any row- or field-level restrictions to each chunk and its vector record. If a document is re-chunked, permissions must be copied or recomputed, not inferred from a neighboring record. Keep the metadata in the same transaction or verification process as the index update so a newly visible chunk cannot appear without its policy attributes.
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At query time, derive the caller’s identity and effective permissions from your authenticated session or service identity. Apply tenant and classification filters in the retrieval layer, then perform a second authorization check on the selected results before context assembly. Security trimming, permission-aware indexes, managed identities, and metadata filters are established secure-RAG practices.
Never rely on a post-generation refusal to repair an access-control error. If a result cannot be authorized, remove it before prompting and record the failed check as a security event. A retrieval service should return only content the current user or agent is already entitled to read, even if the model would have refused to quote it.
Protect against cross-tenant and confused-deputy failures
- Require a tenant filter on every retrieval request; do not make it optional or caller-supplied without server-side verification.
- Use separate namespaces or indexes where practical, while retaining per-record checks for defense in depth.
- Pass the end-user identity to downstream services instead of allowing a broad service account to stand in for everyone.
- Test deletion and permission revocation paths. A removed document must stop being retrievable after your documented propagation interval.
3. Assemble a bounded, visibly untrusted context
Separate instructions from evidence
Keep system and developer instructions in a distinct message or field from retrieved material. Delimit each result and label it as data to analyze, not instructions to follow. Include source identifiers and citations outside the quoted text so a chunk cannot impersonate your control message.
Prompt wording and delimiters are useful layers, but they are not an enforcement boundary. Model attention varies, and a malicious instruction can be persuasive even when it appears inside quotation marks. Test any reinforcement placed after retrieved content, but do not substitute it for authorization or action controls.
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As an initial configuration to measure, OWASP gives 3–5 chunks and 2,000–4,000 tokens as a reasonable default example. This is not a universal safe limit or a security standard. Tune the number and size for your task, model context window, citation requirements, and observed attack and false-positive behavior. Smaller, higher-quality context reduces opportunities for context flooding and makes review easier.
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- Set a maximum number of chunks and a maximum token or character budget.
- Prefer passages with clear provenance and relevance over indiscriminately filling the window.
- Reject or quarantine oversized, duplicated, or mutually contradictory results rather than silently truncating security-relevant text.
- Keep conversation memory under the same provenance and authorization rules as fresh retrievals.
4. Screen content without mistaking detection for enforcement
Use multiple signals
Run deterministic checks for known dangerous patterns, hidden markup, suspicious encodings, unexpected tool syntax, and policy violations. Add a classifier, content-safety model, or prompt-shield service to identify less obvious attempts. Microsoft’s guidance recommends combining probabilistic and deterministic defenses, isolating untrusted content, and monitoring at runtime.
Both categories have limitations. Pattern filters miss paraphrases, obfuscated text, and attacks that contain no trigger phrase. Guardrail models can be manipulated, can disagree with one another, and can produce false positives. Treat a detector result as a signal that changes routing or risk, not as proof that content is safe.
Choose the response by risk
| Signal | Lower-risk response | Higher-risk response |
|---|---|---|
| Unusual hidden or encoded text | Strip active presentation and mark the passage for review | Quarantine the source and block it from agentic workflows |
| Instruction-like language in a reference document | Include only a bounded excerpt with an untrusted-data label | Exclude the chunk and require human or policy review |
| Detector disagreement | Use a safer retrieval route and increase logging | Fail closed for tool-enabled tasks |
| Repeated suspicious content from one source | Lower source trust and shorten freshness | Disable ingestion or revoke the source until investigated |
Screen fetched pages, uploaded files, retrieved chunks, tool results, memory, and multimodal content. Attackers can move the payload to whichever channel your implementation forgot to inspect.
5. Keep authorization and action policy outside the model
Give tools the minimum capability
Expose only the functions needed for the current task, with narrowly scoped arguments and identities. A search assistant may need read-only document search but not arbitrary HTTP requests, shell execution, account administration, or unrestricted database access. Keep API keys, credentials, and detailed permission data out of prompts whenever possible.
Authorize every invocation at execution time
- Parse the model’s proposed tool call against a strict schema.
- Allow-list the tool name and reject unknown or dynamically constructed functions.
- Validate argument types, ranges, destinations, and resource identifiers.
- Resolve the caller’s current permissions in application code; do not trust a role or approval claim in model text.
- Apply rate, quota, and transaction limits.
- Require action-specific confirmation for destructive, external, financial, privacy-sensitive, or irreversible operations.
- Log the request, policy decision, identity, and result without retaining secrets unnecessarily.
“The user asked me to” is not sufficient evidence of authorization, and a retrieved document cannot authorize an action. Approval should name the specific operation, target, and material consequences rather than granting a broad session-level exception.
Use privilege separation for high-impact agents
One option is a read-only, quarantined component that analyzes untrusted content and emits a structured recommendation. A separate policy service then decides whether a privileged action is permissible. This architecture can contain a compromised model, but it is not a guarantee; the interface, identity propagation, and approval workflow become new attack surfaces.
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6. Validate outputs and fail safely
Treat every output as untrusted
Validate structured responses against a strict schema and business rules before passing them to another system. Check enum values, identifiers, numeric bounds, ownership, and references to authorized resources. Escape or sanitize HTML before rendering. Use parameterized database operations and typed API clients instead of concatenating model text into SQL, shell commands, URLs, or templates.
Keyword-only output filtering is insufficient. A harmful operation can be expressed without a blocked word, and a benign response can contain a flagged term. Validation must understand the destination and the operation’s policy.
Define fail-closed behavior
Decide what happens when retrieval, authorization, screening, model validation, or monitoring is unavailable. For a secured search answer, do not silently fall back to an unprotected model-only response when authorized retrieval fails. Return a clear status such as “unable to answer securely right now,” preserve the incident record, and provide a recovery path. For low-risk informational tasks, you may choose a restricted no-tool mode, but document that downgrade and test it.
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Build an adversarial test set
Include poisoned documents, hostile web pages, hidden or encoded instructions, payloads split across chunks, attempts to expose another tenant’s data, and attempts to trigger tools. Add attacks that use paraphrase, images, formatting, multilingual text, and misleading authority claims. Include benign documents that contain ordinary imperative language so you can measure overblocking. Do not rely only on lists of obvious keywords.
Use harmless substitutes and explicit success criteria
During testing, use synthetic sensitive records and instrumented tools that cannot affect production. Record whether unauthorized content reached context, whether a tool was proposed, whether execution was blocked, and whether the user received a safe explanation. Measure both attacks blocked and legitimate tasks incorrectly blocked. The cited guidance provides no universal pass threshold, attack-success rate, latency target, or false-positive rate; establish thresholds for your application’s risk.
Retest every material change
- Model, system prompt, tool schema, or guardrail update
- Embedding model, chunking strategy, ranking algorithm, or context budget change
- New connector, parser, file type, memory store, or multimodal input
- Tenant, classification, retention, or permission-policy change
- Fallback, timeout, retry, or incident-response change
8. Monitor behavior and prepare incident response
Capture enough telemetry to reconstruct a decision without creating a second data-leak repository. Useful events include source and document identifiers, authorization outcomes, detector decisions, context-size and truncation events, tool authorization results, approvals, output-validation failures, and unusual plans or action sequences. Redact secrets and minimize storage of raw prompts or sensitive document text; retain references and hashes when they are sufficient for investigation.
Monitor for plan drift, sudden changes in detector decisions, repeated authorization failures, unusual cross-source requests, spikes in tool calls, and attempts to access resources outside a user’s normal scope. Alerts should route to people who can revoke a source, disable a connector, remove a tool, or force read-only mode.
Your response playbook should include:
- Stop or quarantine the affected source, connector, tool, or tenant scope.
- Preserve relevant logs, source versions, policy decisions, and model configuration.
- Determine whether unauthorized content entered context or an action executed.
- Revoke credentials or permissions that may have been exposed.
- Reindex or purge poisoned chunks and invalidate affected caches.
- Notify impacted users or owners according to your incident policy.
- Add a regression test and review why a preceding control did not contain the failure.
Compare controls by enforcement point and blast radius
| Control | Enforcement authority | What it contains | Typical trade-offs |
|---|---|---|---|
| Provenance and ingestion validation | Application policy and parser controls | Bad, altered, or active content before indexing | Ingestion latency, parser complexity, review workload |
| Permission-aware retrieval | Deterministic identity and metadata policy | Cross-tenant and unauthorized disclosure | Metadata quality, index design, revocation propagation |
| Context delimiting and limits | Application assembly plus model instruction | Context flooding and confusion | Less recall, truncation decisions, task-specific tuning |
| Pattern and classifier screening | Deterministic and probabilistic detectors | Known or suspicious payloads | Misses, false positives, compute and latency cost |
| Least-privilege tools | Runtime identity and service policy | Impact when the model is manipulated | Reduced automation, more integration work |
| Action-specific approval | Human or policy decision outside the model | Irreversible or high-impact operations | User friction and approval delays |
| Output validation | Typed application code and destination controls | Injection into downstream systems | Schema maintenance and rejected edge cases |
| Monitoring and response | Operational process and telemetry | Duration and spread of incidents | Logging cost, privacy obligations, alert fatigue |
Implementation checklist
- Threat model: list every external source, trust transition, tool, memory store, and downstream sink.
- Identity: authenticate users and services; propagate the effective caller identity.
- Provenance: retain source, owner, tenant, integrity, time, parser, and citation metadata per chunk.
- Ingestion: constrain formats and sizes; safely process active, hidden, and encoded content; quarantine anomalies.
- Retrieval: enforce tenant, classification, and document permissions before ranking results into context.
- Context: delimit untrusted data, cap chunks and tokens, and prevent silent security downgrades.
- Detection: combine deterministic and probabilistic signals across files, pages, chunks, tools, memory, and multimodal inputs.
- Tools: allow-list functions, validate arguments, use least privilege, and require approval for high-risk actions.
- Outputs: enforce schemas and business rules; sanitize rendering; use parameterized database and API operations.
- Failure modes: fail closed or enter an explicitly restricted mode when a security control is unavailable.
- Testing: maintain adversarial and benign corpora, synthetic secrets, instrumented tools, and regression tests.
- Operations: monitor policy outcomes and plan drift, minimize sensitive logs, and rehearse source quarantine and credential revocation.
Common failure scenarios and fixes
“The model was told not to follow retrieved instructions, but it still did.”
Prompt wording is only one layer. Add source screening and context limits, then ensure that any resulting tool call must pass independent schema, identity, permission, and approval checks. If the task is high risk, remove tools entirely while untrusted content is being analyzed.
“A user saw a document from another tenant.”
Investigate index metadata, query filters, cache keys, and service identities. Enforce tenant and document authorization before context assembly, invalidate cached results, test revocation, and treat the failed check as an incident rather than a model-quality problem.
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“A detector blocks too many legitimate documents.”
Measure benign false positives by source type and task. Prefer quarantine, reduced context, or read-only routing for moderate-risk signals instead of a universal block. Keep deterministic policy gates for permissions and actions; tune probabilistic detectors without weakening those gates.
“The secured retriever is down, so the product falls back to a general model.”
That fallback silently removes the protection that users were promised. Return a controlled unavailable response or a separately tested no-retrieval, no-tool mode, and expose the degraded state to operators and users.
What resistance means in practice
No architecture can promise that a language model will never be influenced by hostile content. Resistance means an attacker must cross several independently enforced boundaries, and a missed detection still cannot read unauthorized data or perform a high-impact action. Build those boundaries around provenance, authorization, least privilege, validation, safe failure, and continuous adversarial testing. The model can help interpret search results; only your application should decide what the caller may see and what the system may do.
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