TL;DR: GenAI and LLM-assisted development can accelerate delivery while also importing vulnerable patterns, according to StackHawk, and runtime testing becomes more important because static analysis alone cannot observe non-deterministic outputs or prompt-driven behaviour. The governance problem is no longer whether AI can write code, but whether security teams can verify what that code does before production.
At a glance
What this is: StackHawk argues that AI-generated code and LLM-backed APIs need runtime testing because static trust cannot expose prompt-driven behaviour or non-deterministic outputs.
Why it matters: For IAM, NHI, and broader security teams, the issue is governance of what code and APIs do at runtime, not whether AI accelerated delivery in the first place.
Context
AI-assisted development changes the assurance problem because the code path is no longer fully visible at authoring time. Static review can still catch known bad patterns, but it cannot reliably prove how a prompt-influenced model or generated component will behave once inputs, context, and API responses interact in production. For security teams, that creates a trust gap between what was generated and what is actually deployed.
The identity angle is indirect but real: AI-generated code often calls APIs, handles tokens, and relies on service credentials that determine whether the resulting workload can operate safely. That means governance has to cover both the generated application logic and the machine identity paths it uses. In this topic, runtime verification is the control that narrows the gap between code creation and operational trust.
Key questions
Q: What breaks when AI coding tools are trusted without strong verification?
A: What breaks first is consistency. Without strong verification, teams inherit hidden defects, uneven quality, and release decisions based on confidence rather than evidence. Over time, that creates verification debt, where more code is generated faster than the organisation can responsibly assess and approve it.
Q: Why do LLM-backed APIs create more testing risk than ordinary code?
A: LLM-backed APIs are not fully deterministic, so the same feature can respond differently across runs, prompts, and contexts. That means a single source-level approval cannot prove how the service will behave in production. The risk is not just bad code, but unpredictable behaviour that needs repeated dynamic validation.
Q: How can security teams tell whether AI-generated code is actually safe?
A: They should look for security regression evidence, not just test pass rates. A codebase is safer only when the agent’s output survives threat-informed checks, review of privileged paths, and validation against the vulnerability classes most likely to recur in that repository.
Q: What is the difference between static analysis and runtime testing for AI-generated code?
A: Static analysis examines source or build artefacts for known patterns, while runtime testing observes what the application actually does when it is live. For AI-generated code, that difference matters because many failures only emerge through prompt-driven behaviour, non-deterministic outputs, or API interactions. Both controls help, but only runtime testing confirms observed behaviour.
Technical breakdown
Why static analysis misses AI-generated behaviour
Static application security testing inspects source code or build artefacts for known patterns, but AI-generated code can be structurally valid while still behaving badly once it receives real inputs. Large language model outputs are probabilistic, so the same prompt can produce different logic across runs, and that variability is not visible in a single source scan. In practice, static tools can help with syntax, common anti-patterns, and dependency issues, but they cannot observe how the application responds to specific prompts, malformed payloads, or chained API calls. That is why assurance has to move closer to execution, where behaviour becomes testable rather than assumed.
Practical implication: Use runtime testing to validate AI-assisted code paths that static review cannot reliably prove safe.
How DAST fits LLM and API security
Dynamic application security testing sends inputs to a running application and inspects responses for weaknesses. That matters for LLM-backed systems because the risk often appears in how the application processes prompts, returns outputs, or exposes downstream API behaviour. If the model or wrapper logic alters responses based on context, the security question is not just whether the code compiles, but whether it leaks data, enables injection, or mishandles access-controlled flows. DAST is a better fit than source-only review when the security property you care about only appears after the application is alive and interacting with external data.
Practical implication: Test the live API and prompt surface before production so output-driven failures are caught where they occur.
Why non-determinism changes the assurance model
Non-determinism means a model does not always produce the same output for the same input, especially when prompts, temperature, or context differ. That breaks a common assumption behind conventional testing, which expects a repeatable result from a fixed code path. In AI-assisted systems, security teams need multiple runs with varied inputs to understand the range of possible behaviours and to identify responses that cross a policy boundary. This is less like checking a single function and more like exploring a behavioural envelope. The governance challenge is therefore probabilistic assurance, not absolute certainty.
Practical implication: Build test cases that vary prompts and inputs so you can measure the safe operating range of the AI-assisted feature.
Threat narrative
Attacker objective: Exploit the gap between assumed code behaviour and observed runtime behaviour to reach data exposure or application abuse.
- Entry occurs when AI-generated or prompt-influenced code is merged into an application without runtime validation of its actual behaviour.
- Credential or data exposure follows when the running application processes inputs differently than static review predicted, especially across API calls and prompt handling.
- Impact is the deployment of code that appears safe on paper but leaks data, mishandles access, or introduces exploitable behaviour in production.
Breaches seen in the wild
- Samsung ChatGPT leak 2023: Samsung staff pasted chip source code and meeting notes into ChatGPT weeks after it was allowed, leading Samsung to restrict generative AI tools.
- DeepSeek database exposure 2025: An unauthenticated DeepSeek ClickHouse database exposed over a million log lines with plaintext chat history and API keys in 2025.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Runtime trust is now the real control boundary for AI-generated code. Source review still matters, but AI-assisted development creates behaviour that only appears once prompts, inputs, and API responses interact in production. That makes runtime verification the point where security teams learn whether the generated logic is safe enough to ship.
Static trust assumptions fail when code generation is probabilistic. A single approval cannot establish how an LLM-backed feature will behave across different prompts or data combinations. The governance model has to shift from assuming one correct implementation to testing a range of observable behaviours before release.
AI-generated code widens the gap between application security and machine identity governance. If generated code uses tokens, service accounts, or API keys, the security question is not only what the code does but what identities it can reach at runtime. That is where NHI oversight becomes part of AI application assurance, not a separate concern.
LLM API assurance needs behavioural controls, not document trust. Teams cannot rely on design intent or code comments to prove safety when output is non-deterministic. Runtime testing, access boundary checks, and prompt-aware validation are the only defensible way to show that the deployed feature behaves within expected limits.
AI security debt accumulates quickly when generated code is treated as inherently reviewable. The more teams copy and paste model output into delivery pipelines, the more they inherit hidden assumptions about data flow, prompt handling, and downstream access. Practitioners should treat every generated path as an unverified behavioural change until runtime evidence says otherwise.
From our research library:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- Read next: Agentic AI Security Guide
What this signals
AI security debt begins at release, not at model selection. Teams that approve generated code on the strength of source review alone are deferring the real security decision until runtime. The safer posture is to treat each AI-assisted change as a behavioural change that needs evidence, not trust.
Runtime verification is becoming a baseline expectation for AI-assisted delivery. This is especially true where generated code sits on top of API credentials, tokens, or service accounts, because behaviour and identity are now coupled at execution time. That coupling is why machine identity governance belongs in the same conversation as AI code assurance.
AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026. That scale signal reinforces the same operational lesson: when AI is in the delivery path, secrets and runtime behaviour need governance together.
For practitioners
- Add runtime verification to AI-assisted release gates Require dynamic testing for any feature that includes generated code, prompt handling, or LLM-backed APIs before it reaches production. Static review can stay in the pipeline, but it should not be the final control for behaviour that only appears at execution time.
- Test multiple prompt variants and inputs Run repeated tests with varied prompts, malformed data, and boundary inputs to capture the range of outputs an LLM-backed feature can produce. Treat unexpected but valid outputs as part of the security surface, not as edge cases to ignore.
- Review token and service-account exposure in generated code Check whether AI-assisted code introduces new calls to APIs, secrets stores, or service credentials that were not part of the original design. Verify that machine identities used by the feature are scoped to the minimum runtime permissions needed.
- Separate source trust from deployment trust Keep code review, dependency scanning, and runtime testing as distinct controls so a clean-looking pull request does not imply production safety. The final trust decision should rest on observed runtime behaviour, not on where the code came from.
Key takeaways
- AI-generated code creates a trust problem that source review alone cannot solve, because the security-relevant behaviour only appears at runtime.
- LLM-backed APIs are harder to assure than ordinary application code because their outputs are non-deterministic and need repeated behavioural testing.
- Teams that want to ship AI-assisted features safely need runtime evidence, scoped machine identities, and release gates that verify actual behaviour.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP API Security Top 10 and MITRE ATT&CK address the attack and risk surface, while OWASP ASVS and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP API Security Top 10 | API8 — Security Misconfiguration | The article focuses on runtime API behaviour and what source review misses in live application flows. |
| Recommendation — Test live API behaviour for misconfigurations and unsafe responses that only appear at runtime. | ||
| OWASP ASVS | V16 — Security Logging and Error Handling | Runtime validation depends on observing how AI-backed features actually respond and fail. |
| Recommendation — Instrument AI-assisted features so runtime failures and suspicious outputs are visible in testing. | ||
| NIST CSF 2.0 | PR.DS-10 — Data in transit is protected | Generated code often moves data through APIs where runtime handling, not static intent, controls exposure. |
| Recommendation — Verify that AI-assisted code protects data as it moves through live API and prompt flows. | ||
| MITRE ATT&CK | TA0006;TA0010 — Credential Access; Exfiltration | The article's risk is unsafe runtime behaviour leading to secret exposure or data leakage. |
| Recommendation — Map AI runtime testing to credential access and exfiltration paths that generated code could expose. | ||
Key terms
- Performance Testing: Performance testing measures how an API behaves under load, including latency, throughput, and stability. It helps teams understand whether services remain responsive as traffic rises, and whether bottlenecks or regressions appear before users experience slowdowns or failures.
- Non-deterministic output: Non-deterministic output means the same prompt or input can produce different responses across runs. In security terms, that makes one-off validation weak because the risk lies in the range of behaviours the system may exhibit, not a single predictable result.
- Dynamic Application Security Testing: Dynamic Application Security Testing evaluates a running application from the outside to identify weaknesses that only appear under real execution conditions. It is useful for validating authentication, session handling, and API behaviour, especially where configuration and integrations change how the system actually responds to attack.
- Machine Identity: The digital identity of a machine, device, or workload, such as a server, container, or VM, used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
Deepen your knowledge
NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, agentic AI identity, machine identity security, IAM, and secrets management. It helps practitioners connect runtime trust decisions to the identity controls their programmes already own.
Published by the NHIMG editorial team on August 2, 2026.
Updated on October 13, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org