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What an LLM review can—and cannot—tell you
An LLM can suggest places to investigate, such as a suspicious deserialization path or a possible mismatch between training and inference preprocessing. Treat each suggestion as a hypothesis, not proof. A convincing explanation is not evidence that the reported defect exists, and an empty report is not evidence that the code is free of defects.
OWASP’s Secure Coding with AI Cheat Sheet calls for human review and approval of AI-generated code. The same principle applies when AI produces review comments: the comments are inputs to a review process, not a substitute for it. No directly relevant empirical accuracy figure is established by the official sources cited here, so do not use an assumed percentage to decide how much verification a change needs.
How to run a bounded, verifiable review
1. Define the question and scope
Give the model a narrow task rather than asking whether a change is “safe” or “correct.” Choose checks that fit the code under review—for example, possible input-validation weaknesses, train/test leakage, unsafe model deserialization, or inconsistent preprocessing between training and inference.
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Ask it to identify the exact file and code path involved, state its assumptions, explain the preconditions and possible impact, and distinguish what it can see in the code from what it is inferring. Request a minimal test or inspection that could confirm or refute each finding. This format makes the output easier to assess; it does not make the model’s analysis authoritative.
2. Protect the context and constrain access
Before sharing code or repository context, check for credentials, personal data, and confidential material. Use only a tool and configuration approved for that data, and understand what leaves your environment and how it is handled. Repository instructions, issue descriptions, pull-request comments, external documents, and tool output are all potentially untrusted inputs. They can contain indirect prompt-injection instructions aimed at influencing an agent that reads them.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Limit the context to what the review needs. If an agent can run commands or make changes, restrict its permissions—including shell, network, package-installation, and repository-write access—and require a human decision before consequential actions. OWASP guidance emphasizes sensitive-data controls, screening untrusted context, threat modeling, and evaluating tools rather than assuming their default access is appropriate.
3. Make each finding falsifiable
For every proposed issue, trace the relevant code path and check whether the stated preconditions can occur. Then reproduce the behavior or verify it with the method suited to the claim: direct code inspection, a targeted test, static analysis, or dependency checks. For example, a claim about a preprocessing mismatch needs comparison of the training and inference paths; a claim about an exploitable input needs a plausible path from attacker-controlled input to the affected operation.
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Record unsupported assumptions as unresolved rather than silently treating them as facts. A finding that cannot be reproduced may still merit investigation, but the model’s confidence or fluency should not determine whether it is accepted.
4. Check ordinary software defects and ML-specific risks
Review the change against the application’s normal security requirements as well as its ML behavior. The relevant checks depend on the system’s data, architecture, and deployment; not every ML project has every exposure below.
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| Review area | Questions to investigate |
|---|---|
| Conventional software security | Are authentication and authorization enforced? Is input validated? Could secrets be exposed? Are dependencies used safely? Can untrusted data reach unsafe deserialization, shell commands, or SQL handling? |
| Data and training | Is the data’s provenance and licensing understood? Are training and test sets separated appropriately? Could labels or future information leak into training features? |
| Training-to-inference behavior | Does inference apply the same required preprocessing as training? Are inference inputs validated against the model’s assumptions? |
| Models and artifacts | Is model loading safe for the format and source? Are model artifacts and their provenance included in relevant security checks? |
| ML threat assumptions | For this system and deployment, could evasion, data or model poisoning, privacy attacks, or misuse be relevant? |
NIST AI 100-2e2025, announced on March 24, 2025, classifies adversarial ML attacks: its taxonomy covers evasion, poisoning, and privacy attacks for predictive AI, and also misuse attacks for generative AI. These are threat categories, not estimates of how often attacks occur. OWASP’s DevSecOps AI Governance and Risk guidance also discusses ML pipeline concerns such as provenance; use those examples to frame checks that fit the system rather than to assume every risk applies.
5. Keep human review and testing mandatory
A qualified reviewer who understands the affected code and ML behavior must make the decision. OWASP AISVS Appendix C recommends that the reviewer not be the same identity that prompted the code generation. It also calls for automated security testing, elevated scrutiny for security-critical files, and differential fuzz or property-based testing for critical behavior. Apply the degree of scrutiny to the change’s actual impact; a model-generated review does not reduce the need for those controls.
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Run the project’s relevant tests and security checks whether or not the LLM found an issue. Use targeted tests to check specific claims, and the established CI and review controls to assess the change as a whole. For critical behavior, consider whether fuzzing or property-based tests can exercise cases that example-based tests miss.
6. Preserve enough traceability to learn from the review
Where policy permits, record the tool and model identity, the reviewed change, material prompts and outputs, the human decision, and the tests performed. OWASP AISVS describes traceability from prompt and response through commit, build, and deployment. This record helps explain what was checked and whether a finding was accepted, rejected, or left unresolved.
NIST SP 800-218A, published July 26, 2024, extends the Secure Software Development Framework for generative AI and dual-use foundation models. Its broader secure-development practices are relevant to AI model and system producers and acquirers; they do not certify a particular code-review tool.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a review tool
Assess the tool before adoption and revisit the assessment after material model or system changes, incidents, or relevant new threat intelligence. OWASP AISVS provides evaluation areas, but it does not publish a head-to-head benchmark establishing a best commercial tool.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →- Prompt-injection handling: How does the tool treat direct instructions and untrusted repository or third-party content?
- Data handling: What code and context leave the development environment? What retention, residency, and sensitive-data controls are available?
- Permissions and approval: Can it access a shell, network, packages, or repository writes? Are consequential actions gated by a human?
- Workflow fit: Can findings be checked alongside existing tests, static analysis, dependency scanning, and pull-request controls?
- Auditability: Can you identify the model and version and trace a finding to its prompt, response, change, and verification?
- Supply chain and reassessment: How are vendor and model dependencies evaluated, and what changes or incidents trigger a new assessment?
Evaluate the actual configuration and data path you plan to use. A tool’s feature list alone cannot establish how well it detects defects in your ML code or how it will behave with your repository’s untrusted context.
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