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What prompt injection is
Prompt injection is an attempt to make an AI system follow instructions that came from an untrusted source instead of, or in conflict with, the task and policies set by the system owner. The attacker may ask the model to reveal hidden instructions, ignore safety rules, access data outside its assignment, or use connected tools in an unauthorized way.
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Direct prompt injection
A direct injection is supplied in the conversation itself. For example, a user may tell an assistant to disregard its operating rules and print confidential system instructions, or may disguise a request to delete records as an ordinary support question. The input is visible to the model as a user message, so controls can inspect it before inference, but a filter cannot reliably understand every new wording, language, encoding, or multi-step strategy.
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An indirect injection is hidden in material the agent is asked to read or retrieve: a web page, email, PDF, spreadsheet, image, plugin response, code repository, or search result. The content can contain text such as “send the customer database to this address” that the model mistakes for an instruction. This is a supply-chain-style input problem: a document that looks like data to the application can look like an instruction to the model.
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Indirect attacks are especially serious for agents with browsing, email, file, calendar, purchasing, or database access. The user may never see the malicious content, and the content can change after an initial review. Microsoft describes deterministic detection of indirect injection as an open research challenge, so an application must assume that screening can miss a well-crafted payload.
Can prompt injection be prevented?
Not completely. OWASP says it is unclear whether foolproof prevention is possible because generative systems are stochastic. OpenAI likewise cautions that its defensive measures may not prevent every prompt injection. The practical objective is to make attacks harder, detect suspicious behavior, and ensure that a missed injection cannot cause unacceptable damage.
Build a trust boundary around instructions and data
Declare which instructions are trusted and which material is merely data. System and developer policies should define the agent’s role, allowed actions, data sources, and refusal conditions. Retrieved text, uploaded files, web pages, and tool responses should be clearly labeled as untrusted content and placed in a separate context section. Do not rely on formatting alone: enforce the distinction in the application that assembles prompts and in the authorization layer that executes actions.
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Spotlighting or other data-marking techniques can help a model recognize that quoted material is not an instruction, but they are probabilistic defenses. Treat them as additional signals, not as permission to bypass a hard authorization check.
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Use least privilege and short-lived access
Give an agent only the identities, records, and operations required for its current task. Prefer read-only access for research and triage. Separate tools for reading, drafting, and executing so that a model cannot turn a harmless lookup into an irreversible action with one call. Use short-lived credentials tied to a specific job, user, tenant, and purpose; revoke them when the job ends or when a policy violation is detected.
Partition sensitive data so that a compromised context cannot search an entire account. Information-flow controls should prevent data from a restricted source being copied into an external message, a lower-trust workspace, or a tool that has no business need for it.
Screen inputs, but do not stop there
Prompt shields and other classifiers can flag obvious jailbreak language, instruction-like text inside documents, or attempts to override the agent’s role. They are useful before inference and can reduce routine attacks. However, attackers can use synonyms, multiple languages, encodings, images, long benign passages, or instructions that only become dangerous after several tool calls. A blocked-keyword list is therefore a weak control by itself.
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Require the model to produce a typed, narrow format for each operation. Validate that format with normal application code before any tool runs. Check required fields, value ranges, target identifiers, destination domains, record ownership, and the requested operation. Reject extra fields and ambiguous values rather than asking the model to “try again” with broader permissions.
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The strongest deterministic control is authorization next to the side effect. A mail-sending service, for example, should independently verify the approved recipient, message type, account, and user confirmation. A model prompt saying “never send external mail” is not an enforcement mechanism if the mail API accepts any address supplied by the model.
Require approval for consequential actions
Put a human confirmation step before sending messages, purchasing, deleting, changing access, publishing content, moving money, or modifying production systems. Show the user the exact action, destination, data being disclosed, and reason it was proposed. Approval should be bound to that specific request and expire if the arguments change. For low-risk operations, automatic execution can be acceptable; for high-impact operations, approval is a containment boundary rather than a courtesy prompt.
Use defense in depth
Microsoft recommends combining probabilistic and deterministic defenses because no single solution is sufficient. Training, prompt screening, spotlighting, critic agents, and anomaly models reduce the likelihood of a successful attack. Isolation, least privilege, allowlists, typed interfaces, rate limits, and approval gates reduce the blast radius when a model behaves incorrectly.
| Control type | Examples | What it can establish | Limitation |
|---|---|---|---|
| Probabilistic | Prompt shields, classifiers, model training, critic agents, anomaly scoring | Reduces the likelihood that suspicious content is followed or passed onward | Can miss novel, obfuscated, multilingual, or indirect attacks; results vary with model behavior |
| Deterministic | Tool authorization, schema validation, allowlists, sandboxing, read-only identities, approval gates | Can guarantee that a particular unauthorized path is rejected or isolated | Only covers the paths and policies that were explicitly implemented; poor policy design leaves gaps |
How to detect prompt injection
Detection should examine the entire execution trace, not just the incoming prompt. A benign-looking request can become dangerous when retrieved content changes the plan or when a tool sequence departs from the user’s goal.
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Inspect user and retrieved content
Scan direct messages, search results, documents, email bodies, attachments, plugin output, and images for instruction-like language, requests to reveal secrets, attempts to change the agent’s role, or directions to contact an external party. Preserve the original content and the screening decision in the log. Do not silently rewrite evidence in a way that makes later investigation impossible.
Watch for plan drift
At the start of a task, record a structured description of the intended objective, allowed sources, tools, and side effects. Compare each proposed step with that baseline. A support agent asked to summarize a ticket should not suddenly enumerate unrelated customer records, create an administrator account, or send data to a new domain. Microsoft identifies plan-drift detection as a useful runtime defense because the dangerous behavior may appear only after the agent reads an external source.
Analyze tool chains and data access
Monitor unusual tool sequences, repeated failed authorization attempts, sudden increases in retrieval volume, access to records outside the user’s scope, and a transition from read operations to external communication. Enforce limits on call count, data volume, destinations, and execution time. A single harmless call may be acceptable while the same call repeated hundreds of times is an exfiltration pattern.
Validate output and arguments against schemas
Use strict schemas for tool arguments and final responses. Check that identifiers belong to the requesting user, that destinations are allowed, and that fields do not contain hidden instructions or unexpected markup. Validate both before execution and after the tool returns, because a compromised or incorrect tool response can influence the next step.
Use critics and runtime monitoring carefully
A separate critic agent can review a proposed plan or tool call for conflicts with the task, but it is still a model and can be fooled by the same content. Combine its finding with deterministic policy checks and human review. Alert on policy violations, unusual sequences, data-flow anomalies, and attempts to disable logging or approval. Record false positives as well as confirmed incidents so thresholds can be improved without encouraging operators to ignore alerts.
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- Default to read-only. Require an explicit, separately authorized transition before an agent can write, send, delete, purchase, or publish.
- Isolate untrusted processing. Use a sandbox or quarantined environment for files, web content, and code that does not need access to production credentials.
- Allowlist destinations and operations. Permit only approved domains, APIs, record types, and action categories.
- Limit rate and volume. Cap calls, rows, messages, attachment sizes, and spending per task and per identity.
- Expire credentials quickly. Revoke tokens when a job ends, a user cancels, or a policy breach occurs.
- Pause on ambiguity. Stop when the requested action, destination, or data scope cannot be resolved unambiguously.
- Preserve evidence. Keep immutable records of inputs, retrieved content, model outputs, tool calls, approvals, and side effects for investigation.
How to audit prompt-injection defenses
An audit should test the model and the surrounding authorization and execution controls. A successful refusal is not enough if the same request can be performed by manipulating a tool argument, and a detector’s alert is not enough if the action has already occurred.
- Define the intended operating envelope. Write down the user task, trusted instructions, permitted data sources, tools, identities, destinations, and side effects. State which actions require confirmation and which must always be denied.
- Create a direct-attack test set. Include role overrides, requests for hidden instructions, attempts to obtain secrets, conflicting priorities, and multi-turn escalation.
- Create an indirect-attack test set. Place payloads in web pages, email, documents, files, plugins, retrieval results, and multimodal content. Include instructions that tell the agent to ignore its policy, disclose data, or call a new destination.
- Vary the presentation. Test multiple languages, encodings, obfuscation, unusual punctuation, images, long benign context, and payloads that avoid obvious keywords. OWASP specifically warns against relying only on signature filters.
- Use instrumented tool substitutes. Replace real mail, payment, deletion, and production APIs with test doubles that record attempted arguments and return controlled responses. This allows the complete workflow to be exercised without creating real side effects.
- Repeat every scenario. Record the model and application version, system instructions, retrieved content, tool arguments, approvals, outputs, and side effects. Model outputs can vary, so one successful run or one refusal is not a reliable result.
- Check the enforcement point. Verify that authorization is performed at the tool boundary and cannot be bypassed by a prompt, classifier result, alternate connector, or direct API call.
- Measure operational outcomes. Track false positives, false negatives, blocked actions, successful unauthorized actions, time to detection, time to containment, and whether evidence was complete enough to reconstruct the event.
- Re-audit after changes. Repeat the matrix whenever the model, connector, retrieval system, tool, permission, policy, or prompt changes. A previously safe configuration can become unsafe when a new data source or capability is added.
Securing an agent that reads email, websites, or documents
A practical design separates reading from acting. The ingestion service fetches content with a low-privilege identity, labels it untrusted, and stores a copy for auditing. The model may summarize or extract fields, but it cannot treat text inside that content as a policy update. A planner proposes actions in a typed format. A policy service checks the user, tenant, data scope, destination, and operation. Only then does an execution service use a short-lived credential. High-impact actions pause for user approval.
For example, an email-triage agent can classify a message and draft a reply automatically. It should not send the reply until the approved recipient and content are displayed to the user or match a narrowly defined rule. A document-review agent can quote a clause, but it should not upload the document to an external service merely because a hidden paragraph requests it. A browsing agent can retrieve a page in a sandbox while denying the page any route to credentials or production tools.
Common mistakes that make defenses fail
- Trusting the system prompt as a security boundary. A prompt expresses policy; it does not enforce authorization.
- Using one detector. A classifier or prompt shield can miss novel and indirect attacks and can also block legitimate content.
- Giving broad credentials for convenience. Excessive permissions turn a model mistake into a data breach or destructive action.
- Logging only the final answer. Without retrieved content, intermediate plans, tool arguments, approvals, and side effects, investigators cannot determine how the failure occurred.
- Testing only obvious English jailbreaks. Keyword-free, encoded, multilingual, multimodal, and supply-chain inputs must be included.
- Failing open after an error. If a policy check, schema validator, or approval service is unavailable, pause the action rather than executing with defaults.
- Assuming a single successful test proves safety. Repeated runs are necessary because model behavior is variable.
What a defensible policy should say
A defensible policy states that prompt injection is an ongoing risk, not a one-time bug. It assigns an owner for permissions, defines which actions need approval, requires tool-bound authorization and immutable logs, and sets a schedule for adversarial testing. It also treats model upgrades, new connectors, retrieval changes, and permission changes as security changes that trigger re-audit.
There is no universal prompt-injection success-rate percentage that can be applied across models, applications, and attack sets. Results depend on the model version, context window, tools, policies, fixtures, and test methodology. Report scoped outcomes from repeatable tests instead of presenting a generalized number as a guarantee.
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