AI_Coding_Flaws_Index
01 // THE_STOCHASTIC_ERROR_MODEL
AI doesn't "understand" code logic; it predicts the most likely next token. This Stochastic Error Model means that AI often produces code that looks syntactically perfect but is logically hollow. These ai coding flaws are particularly dangerous because they pass the "it runs" test while failing the "it's secure" test.
02 // ASYNC_RACE_CONDITIONS_IN_AI_CODE
LLMs frequently struggle with complex asynchronous logic in Node.js or Python. We've observed dozens of cases where AI-generated code initializes sensitive sessions or database connections without proper awaiting, leading to race conditions that can leak data between user sessions.
03 // REGEX_REDOS_VULNERABILITIES
When asked to "validate an email" or "parse a URL," AI often suggests complex, unoptimized Regular Expressions. These patterns are frequently susceptible to ReDoS (Regular Expression Denial of Service) attacks, where a carefully crafted input string causes the CPU to spike to 100%, effectively downing your server.
04 // AI_CRYPTOGRAPHIC_MISSTEPS
One of the most persistent ai coding vulnerabilities is the use of insecure cryptographic defaults. AI often uses hardcoded salt values, weak IVs (Initialization Vectors), or legacy algorithms like DES or SHA-1 because they appear frequently in its older training data.
05 // SYSTEMATIC_CODE_VALIDATION
To catch these flaws, your workflow must include:
- Strict ESLint rules for security.
- Unit tests with edge-case fuzzing.
- Comprehensive DAST scanning via SentinelScan.