Guide
AI coding tools like Copilot, ChatGPT, and Claude have changed how fast code ships — and how fast risk ships with it. AI code security means systematically auditing AI-generated and human-written code with automated review before it ships.
AI-generated code isn't worse by default. The risks come from how it's produced and how much of it there is.
Models learn from public code, including outdated or unsafe idioms. Suggestions can reproduce patterns like string-built queries or permissive configurations.
An assistant sees the file in front of it, not your trust boundaries, permission model, or what a given value is allowed to be.
Generated code usually looks plausible and compiles. Subtle flaws — a missing check, an off-by-one in a bound — are easy to approve at a glance.
Faster authoring means more merged changes per week. Each one is a chance for a gap, and the total grows faster than manual review can follow.
Placeholder keys, tokens, and connection strings in generated snippets can slip into commits and history if nobody looks for them.
Suggested packages may be misspelled, abandoned, or malicious, and instructions hidden in files or docs can steer an assistant toward unsafe changes.
These apply to any code. Check for them on every change, whoever wrote it.
| Class | What to look for |
|---|---|
| SQL injection | Untrusted input concatenated into queries instead of bound as parameters. |
| Cross-site scripting (XSS) | User-controlled data rendered as HTML or script without encoding. |
| Hardcoded secrets | API keys, private keys, or passwords written directly into source. |
| Insecure deserialization | Parsing untrusted data into objects in ways that allow code execution or tampering. |
| Broken authentication | Missing session checks, weak token validation, or authorization skipped on a route. |
| Path traversal | File paths built from input, letting callers read or write outside intended directories. |
| Weak cryptography | Deprecated algorithms, static IVs, or homemade crypto in place of vetted libraries. |
| Dependency confusion | Package names that resolve to a public, attacker-controlled package instead of an internal one. |
Assistants suggest code; agents write it, run it, and open pull requests on their own. That creates a new review surface: changes can arrive faster and in greater number than any person drafted them, sometimes touching files nobody asked about.
AI coding agent security starts with a simple rule: every agent-generated pull request gets the same scrutiny as human code — the same checks, the same approval, and no shortcut because the change "came from the tool."
Most teams adopted AI tools to ship faster, and they do. But review teams didn't grow at the same rate. The result is the review gap: more merges per week, the same number of reviewers, and less time per change.
This isn't a question of AI writing worse code. It's arithmetic. When volume outpaces capacity, reviewers skim, security checks get skipped under deadline pressure, and issues that would have been caught in a slower cycle reach production.
Closing the gap means making security review continuous and automated — running on every pull request, regardless of who or what wrote it — so human reviewers spend their time on judgment calls rather than routine checks.
Traditional static analysis matches code against known patterns. It's fast and good at catching things like obvious injection sinks, but it can't tell whether a check is missing for your specific business rules. Automated semantic review reads code in context — how data flows, who can call what, and what the code is meant to enforce — which lets it surface logic flaws and business-logic vulnerabilities that pattern matching misses.
Review each change before merge, so issues are caught while context is fresh.
Periodically scan the whole codebase to catch issues that span files or predate review.
Re-check dependencies and code on a cadence as new vulnerabilities are disclosed.
FYEO Scanner runs automated security audits on every pull request and full-repository scans, returning findings with severity and suggested fixes — with no change to how your team already uses AI tools.
Start a scan