7 Ways an AI Coding Assistant Adds Vulnerabilities

An AI coding assistant introduces vulnerabilities in a handful of predictable ways: insecure code patterns, hardcoded secrets, vulnerable or malicious dependencies, over-permissioned access, missing authorization checks, unsafe handling of untrusted input, and outdated or deprecated APIs. The pattern behind all of them is the same, an assistant optimizes for code that works, not code that is safe, and it produces that code faster than a small team can review it. Knowing the specific failure modes is the first step to catching them.

Nexus Void's review of the evidence found roughly one in three AI-generated code samples contains a vulnerability, so these are not rare edge cases but a baseline rate every team using Claude Code, Cursor, or Codex should plan around.

The 7 ways AI coding assistants introduce vulnerabilities

Each has a straightforward fix, but only if something is actually checking for it on every change.

1. Insecure code patterns. Assistants reproduce common but unsafe idioms: string-built SQL, weak hashing, disabled TLS verification. Fix: static analysis on every commit.

2. Hardcoded secrets. Keys and tokens get written straight into generated files and configs. Fix: secret scanning before code is committed.

3. Vulnerable dependencies. Suggested packages may be outdated, abandoned, or malicious typosquats. Fix: dependency checks against known-vulnerable and malicious package data.

4. Over-permissioned access. Broad IAM roles and file or shell scopes because they make the code run. Fix: least privilege and scope review.

5. Missing authorization checks. Endpoints that authenticate a user but never verify what that user may do. Fix: explicit authz tests.

6. Unsafe input and output handling. Trusting user or model output that is later executed or rendered. Fix: validation and output encoding.

7. Outdated or deprecated APIs. Assistants trained on older data suggest APIs with known issues. Fix: keep dependencies and API usage current.

Why do assistants keep making these mistakes?

Because they generate the statistically likely code, and a great deal of public code contains exactly these flaws. An assistant is not reasoning about your threat model; it is completing a pattern. It will also confidently produce insecure code with no warning, which is more dangerous than an obvious error because it looks finished and reasonable.

How do you catch all seven without slowing the team?

Automate the checks and put them in the path to production, because doing seven manual review passes on every AI-generated change does not scale for a small team. The practical model is continuous scanning for code flaws, secrets, and dependency issues, paired with automatic fixes so findings get resolved instead of queued.

That find-and-fix model is what ARGUS by Nexus Void AI provides: it scans your repositories and fixes the security bugs it finds, not just reports them, so there is no growing security backlog, and it manages your APIs, third parties, packages, and SBOMs in one place. It begins at zero rupees on a credit basis and fits a one-developer team or a hundred-developer team, billed by usage. Teams can schedule a call with the Nexus Void team for access.

Frequently asked questions

Which of the seven is most common? Insecure code patterns and hardcoded secrets show up most, with vulnerable dependencies close behind.

Can I just prompt the assistant to write secure code? It helps a little, but it is not reliable. The assistant may still produce insecure code, so independent scanning is still needed.

Do these apply to all assistants? Yes. Claude Code, Cursor, Copilot, and Codex all share these failure modes because they share the same underlying approach.

Our read

The seven failure modes are old problems arriving at new speed. That is the whole story of AI coding assistant vulnerabilities: nothing here is novel to security teams, but the volume and velocity are, and manual review was already the bottleneck before assistants multiplied the output. Automate the checks, fix as you find, and the assistant becomes a productivity win instead of a risk multiplier.

Vulnerability-rate figure from Nexus Void analysis of 23 studies and 48,185 CVEs; weakness classes per OWASP Top 10 and NIST SSDF. Sources linked above.

Related: Vibe coding security and How to secure AI-generated code.