Key findings:
AI coding is a new category of risk.
AI tools generate code faster than humans can review it, hallucinate packages that don’t exist, and are increasingly targets themselves.
Five common missteps.
From accepting AI-generated code without review to relying on post-production scanning to catch AI-introduced risk.
The risks are different.
Architectural design flaws, privilege escalation paths, and trust-boundary violations are the hardest for traditional scanners to detect.
The problem isn’t the code, it’s the gap.
Security practices built for human-paced development can’t keep up with AI velocity.
If you’re still scanning code after it’s written and auditing access after it’s granted, this cheat sheet is for you.
Traditional AppSec assumes developers write code, pipelines scan it, and teams remediate before deployment. AI-assisted development breaks those assumptions: tools generate code faster than review can keep pace, encourage developers to accept output with less scrutiny, and introduce their own CVEs through misconfigured agents, compromised extensions, and poisoned rule files. Teams that treat AI-generated code like human-written code are measuring the wrong thing.
Inside the cheat sheet: the 5 missteps
- Accepting AI-generated code without security review. Insecure patterns, SQL injection, XSS, hardcoded secrets, broken auth, ship to production unflagged. Safer: embed security testing into the AI workflow, not just downstream CI/CD.
- No visibility into the AI tools, models, and extensions in use. Leadership has no centralized view of active assistants, plugins, MCP servers, and models. Safer: maintain a continuous inventory of AI development tooling.
- Granting AI agents broad access without least-privilege controls. Overprivileged agents expand blast radius for prompt injection and lateral movement. Safer: scope permissions and extend identity governance to non-human agents.
- Treating AI coding as outside the scope of security policy. AI workflows operate in a governance vacuum, no approved model list, no review requirements. Safer: make AI coding governance part of security policy, with SCA on AI-suggested dependencies.
- Relying on post-production scanning to catch AI-introduced risk. AI delivers code faster than scanning can keep pace, and its riskiest flaws evade traditional scanners. Safer: shift to prevention at the point of creation.
By the numbers:
45% of AI-generated code samples introduce OWASP Top 10 vulnerabilities.
10x more security findings from AI-assisted developers vs. non-AI peers.
35 CVEs in a single month (March 2026) directly attributed to AI coding tools.
28% of organizations can reliably trace AI agent actions to a human or system (Cloud Security Alliance). Privilege-escalation paths are up 322% and architectural design flaws up 153%.
