Building an app used to require a dev team; now it often just requires a prompt in Lovable, Replit, or Cursor. While AI coding assistants offer speed and cost-efficiency for MVPs, they introduce a specific class of security risks that traditional human code review usually catches. A new checklist from DEV.to argues that AI-generated code requires the same scrutiny as unreviewed junior developer code, specifically targeting the lack of peer review that allows vulnerabilities to slip straight into production.

The Five Tendencies of AI-Generated Risk

The source material identifies five distinct failure modes unique to LLM-generated code. First, models learn from public repositories that include outdated or insecure patterns, such as SQL string concatenation instead of parameterized queries. Second, knowledge cutoffs mean models often recommend library versions that have since been deprecated or found to contain critical vulnerabilities. Third, and most dangerously, models hallucinate package names; a 2025 study cited in the article found that roughly one in five suggested packages did not exist, a gap attackers exploit through "slopsquatting" by registering malware under those fake names.

Practical Mitigation Strategies

To combat these issues, the checklist breaks security into seven actionable categories. For dependencies, teams must verify every AI-suggested package exists in the official registry and pin versions to avoid blind installs. For code quality, the guide recommends using Software Composition Analysis (SCA) tools like Snyk or Dependabot alongside Static Application Security Testing (SAST) like Semgrep or SonarQube. Crucially, the article notes that automation has limits: scanners cannot detect broken access-control logic or business-logic flaws, such as one user reading another’s data, which still requires human reasoning.

Key Takeaways

  • Verify all package names: One in five AI-suggested packages may be hallucinated, creating slopsquatting risks.
  • Automate review gates: Integrate SAST and SCA tools into CI pipelines to catch hardcoded secrets and known vulnerabilities automatically.
  • Respect knowledge cutoffs: Models may recommend vulnerable library versions that were patched after their training data snapshot.
  • Human review is non-negotiable: Treat AI output as unreviewed junior code, especially for authentication, access control, and business logic.

The Bottom Line

AI accelerates development but removes the safety net of traditional peer review. Teams must treat AI-generated code as unreviewed junior work, using automated scanners for pattern-based errors and human experts for logic-based vulnerabilities.