Scan the code paths that connect untrusted inputs to LLM prompts, then trace how model outputs are used downstream. Prioritise cases where LLM output reaches privileged actions such as database queries, shell commands, or code execution. The goal is to catch directly exploitable flows, not every theoretical concern, so teams need focused tracing and clear validation rules at the application layer.
Tracing Prompt Injection Risk Through Code Paths and Trust Boundaries
Security teams should treat prompt injection review as a dataflow problem, not a keyword scan. The real question is whether untrusted content can influence prompt construction, tool selection, retrieval, or post-processing in ways that alter application behaviour. The OWASP Top 10 for Agentic Applications 2026 is useful here because it frames unsafe model interaction as a control and trust-boundary issue, not just a content-safety issue. In pull requests, that means reviewers should look for input concatenation, indirect prompt assembly, hidden system instruction exposure, and assumptions that model output will remain benign once it leaves the LLM.
The highest-value review target is the path from external input to privileged action. If an LLM output can influence SQL generation, shell execution, file writes, approval logic, or API calls, the security question becomes whether the code enforces an application-layer boundary before those actions occur. In practice, teams often miss the risk because the prompt looks harmless while the downstream sink is the actual point of compromise. In practice, many security teams encounter prompt injection only after a model response has already been wired into a privileged workflow, rather than through intentional review of the prompt-to-action chain.
How It Works in Practice
Effective PR scanning starts by identifying every place the application assembles a prompt from user input, retrieved content, documents, tickets, chat messages, or other external sources. Reviewers should then trace how the resulting LLM output is consumed. A safe-looking prompt wrapper is not enough if the output is later parsed as instructions, passed into a tool router, or used to populate a command or query template. The review should ask two questions: what can influence the model, and what can the model influence after generation?
That workflow is easier to validate when teams separate three layers:
- Untrusted inputs that may contain injected instructions or adversarial text
- Prompt construction logic that decides what the model sees and what context it receives
- Post-generation sinks where model output becomes an action, decision, or secondary prompt
Security teams should also inspect whether the code relies on the model to self-police. That is usually a weak assumption because prompt injection is successful precisely when the application treats model instructions as trustworthy. Stronger patterns include explicit input delimitation, allowlisted tool calls, schema validation on structured output, and hard stops before sensitive actions are executed. For code review, the most important evidence is not whether the prompt mentions “ignore prior instructions,” but whether the application can prove that model output cannot directly trigger a privileged operation without independent checks. NIST AI RMF is relevant when the organisation wants a broader risk-management lens on generative AI behaviour and downstream impact.
This guidance breaks down when the application intentionally allows open-ended agentic action without a clear authorization layer, because then the review is no longer just about prompt injection but about broader delegated execution risk.
When Prompt Injection Review Stops Being a Simple Code-Scan Problem
Tighter filtering and output validation often improve safety, but they also increase engineering overhead and can break legitimate workflows, so teams need to balance attack resistance against usability and maintainability. The standard answer works well for direct prompt-building code, but it becomes less certain when retrieval, orchestration, and tool execution are spread across multiple services. In those cases, the risk is not only in the prompt text itself but in the trust model around the whole interaction chain.
One common edge case is indirect prompt injection through retrieved content. A document, webpage, or ticket may not be user input in the usual sense, yet it can still carry adversarial instructions that the model ingests during retrieval-augmented generation. Another edge case is structured output that is treated as safe because it is JSON or YAML. Structure helps parsing, but it does not make the content trustworthy unless the application validates semantics before using it. There is also a governance difference between a model that summarizes text and a model that can choose tools. The latter has a much higher blast radius, so the review standard should be stricter. Guidance-vs-consensus note: there is broad agreement that downstream authorization matters more than prompt phrasing, but there is not full consensus on how much isolation is enough for autonomous tool use.
If the pull request only changes display text or adds a low-privilege summarization feature, prompt injection risk may be lower than the surrounding AI discussion suggests. If it changes tool routing, function calling, or any path that can affect privileged operations, the review standard should escalate immediately.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and MITRE ATLAS address the attack and risk surface, while NIST AI RMF, CIS Controls v8 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 — Prompt Injection and Instruction Hijacking | Directly addresses unsafe model instruction influence in agentic apps. |
| Recommendation — Trace untrusted inputs into prompts and block instruction hijacking before tool use. | ||
| NIST AI RMF | GV — Govern | Supports governance of generative AI risk and accountability decisions. |
| MAP — Map | Fits identifying where AI inputs, outputs, and downstream harms connect. | |
| MEASURE — Measure | Useful for assessing whether prompt-injection controls actually reduce exposure. | |
| Recommendation — Define review rules that require independent validation before model output drives action. Map prompt and output flows to the actions and assets they can influence. Measure whether unsafe outputs are blocked before they reach privileged sinks. | ||
| MITRE ATLAS | AML.TA0002 — Adversarial Input Manipulation | Covers adversarially crafted inputs that manipulate model behaviour. |
| Recommendation — Hunt for adversarial content that is designed to alter model instructions or outputs. | ||
| CIS Controls v8 | 16 — Application Software Security | Applies to secure review of application logic and input/output handling. |
| 8 — Audit Log Management | Supports retaining evidence of risky prompt and tool actions for investigation. | |
| Recommendation — Review application code so model output cannot reach sensitive operations unchecked. Log prompt, tool, and approval events needed to investigate injection attempts. | ||
| NIST CSF 2.0 | PR.DS — Data Security | Relevant where untrusted content and outputs need protection across data flows. |
| Recommendation — Protect prompt and retrieval data so untrusted content cannot alter trusted actions. | ||
Practitioner Guidance
What to prioritise: Review the exact code paths where untrusted content can reach prompt templates, retrieval context, and post-generation sinks. The highest-value findings usually come from a short list of flows that can influence tools, queries, or execution, not from broad theoretical prompt concerns.
What to verify: Confirm that the application enforces an independent validation step before any model output is converted into an action. The key test is whether a malicious or malformed response can be rejected, constrained, or downgraded before it reaches a privileged operation.
Decision rule: If the model output can affect a security-sensitive action without a separate policy check, treat the pull request as high risk even if the prompt text appears carefully written. If the output stays inside a non-authoritative display or drafting flow, the review can usually stay narrower.
Common mistake: Treating prompt sanitisation as the main control while leaving the downstream sink unguarded. That shortcut misses the point of prompt injection, which is exploitation of trust across the application boundary rather than simply bad text in the prompt.
Practitioner takeaway: The strongest PR reviews focus on where trust changes hands, because prompt injection becomes material only when the application lets untrusted content or model output cross into privileged behaviour.
Related resources from NHI Mgmt Group
- How should security teams review LLM and agent code for prompt injection risks in production workflows?
- How should security teams handle prompt injection in production LLM applications?
- How do security teams reduce the impact of prompt injection in code assistants?
- How should security teams control browser prompt injection risk in LLM tools?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
