Reviews AI-generated code for security failure modes that AI assistants commonly miss: prompt injection risks, credential exposure, dependency vulnerabilities, insecure deserialization, and access control gaps. This skill catches what agents miss when they optimize for functionality over safety, especially in code that handles user input, authentication, or external data.
Use cases
- Reviewing third-party AI-generated code before integrating it into a security-sensitive codebase
- Auditing AI assistant output that handles user authentication, payment processing, or personal data
- Pre-deploy security check for an AI feature where the model output becomes part of a system command or query
- Reviewing code that uses tool-calling or plugin capabilities where malicious inputs could be injected through the model
- Evaluating AI-generated code for compliance with a specific security framework (OWASP Top 10, SOC 2 controls)
Key features
- Check for injection vectors: any place where model output, user input, or external data is executed, evaluated, or used in a system command without validation
- Audit credential and secret handling: verify that API keys, tokens, and passwords are never logged, exposed in error messages, or stored in code
- Review dependency declarations for known vulnerabilities using a CVE database, paying special attention to new dependencies added by the AI
- Verify access control boundaries: confirm that the code enforces authentication and authorization at the appropriate layers, not just at the UI
- Check for insecure deserialization, SQL injection, XXE, and other classic web vulnerabilities that AI assistants frequently introduce when generating boilerplate
When to Use This Skill
- When AI-generated code will handle authentication, authorization, or payment processing
- When AI-generated code includes tool-calling capabilities that could be manipulated through prompt injection
- When a new dependency was added by an AI assistant and has not been reviewed for CVEs
Expected Output
A security review report with findings classified by severity, CVEs for new dependencies, and specific remediation guidance for each finding.
Frequently Asked Questions
- Is AI-generated code less secure than human-written code on average?
- Not necessarily—but it often lacks the security-aware decisions a human makes intuitively. AI assistants generate code that works for the happy path; they do not automatically consider adversarial inputs, timing attacks, or error message disclosure. Security review is required regardless of authorship.
- What is the most common security flaw in AI-generated code?
- Missing input validation—AI assistants frequently generate code that trusts user input without sanitizing it. This manifests as SQL injection, XSS, command injection, and similar injection vulnerabilities that are well-understood but easy to miss in review.
- How does prompt injection change the security review scope for AI features?
- When AI-generated code processes model outputs (e.g., tool-calling results, retrieved documents), you must treat those inputs as potentially attacker-controlled. Apply the same input validation you would for any user-supplied data. Do not assume that because the input came from the model it is trustworthy.
Related
Related
3 Indexed items
OWASP GenAI LLM Top 10 (v1.1) threat review checklist
Maps the authoritative OWASP "Top 10 for Large Language Model Applications" (version 1.1) taxonomy—LLM01 Prompt Injection through LLM10 Model Theft—into an actionable readiness checklist for architects red-teaming Retrieval-Augmented Generation, Agents, plugins, training pipelines, or hosted inference gateways. Official project pages summarize each risk bucket (prompt injection bypassing safeguards, unchecked outputs enabling downstream exploits, poisoned corpora distorting reasoning, abusive workloads starving capacity, brittle supply-chain dependencies, sensitive data resurfacing inside generations, excessively privileged plugins/agents/autonomy, misplaced trust producing compliance failures, loss of proprietary model weights via API abuse). The skill pairs each category with tangible controls (policy, monitoring, toolchain limits) anchored to genai.owasp.org releases rather than anecdotes.
Responsible AI accessibility data review
Turns Microsoft Learn responsible AI modules and accessibility remediation patterns into a checklist for teams shipping generative features that emit images, code, or UI copy. Practitioners verify training-data gaps (for example stereotypical depictions of disabled users), audit metadata labels on inclusive datasets, document human-in-the-loop fixes, and align with published principles that people remain accountable for AI outcomes. The skill references learn.microsoft.com training on responsible AI practices and real-world corrections such as purchasing supplemental multimodal data when model outputs misrepresent blind users—without skipping metadata-layer bias reviews emphasized by ML fairness practitioners.
Receiving code review
Structures how you respond to code review feedback so the review process stays focused, respectful, and productive. This skill separates substantive feedback from nitpicks, tracks follow-ups without losing them, and produces a record that makes merges faster and post-mortems clearer.