Vulnerabilities in AI-Generated Code

AI code assistants like GitHub Copilot, ChatGPT, Cursor, and Claude frequently generate code containing security vulnerabilities. Studies show up to 40% of AI-generated code contains at least one security flaw. Precogs AI pre-LLM filters detect and prevent these flaws before they enter your codebase — including injection attacks, hardcoded secrets, broken authentication, and insecure deserialization patterns.

Verified by Precogs Threat Research

What vulnerabilities are common in AI-generated code?

The most frequent flaws introduced by AI assistants include SQL injection, cross-site scripting (XSS), hardcoded credentials, path traversal, SSRF, and insecure deserialization vulnerabilities. Because LLMs are trained on vast amounts of open-source code, they often reproduce common anti-patterns rather than secure coding standards.

Explore AI-Generated Code by Category

Deep-dive into specific areas of ai-generated code to understand the attack surfaces, common vulnerability patterns, and how Precogs AI provides protection.

By AI Tool

Code Assistants

CursorMCP poisoning, auto-run bypasses, .cursorignore flaws, and agent-mode command injection risks→GitHub CopilotSuggestion injection, code completion vulnerabilities, and licensing issues in generated code→Claude CodeArtifact generation risks, project context leakage, and tool-use safety bypasses→Windsurf / CodeiumCascade agent compound risks, multi-step Flow injection, and enterprise code exfiltration→TabnineTraining data vulnerability reproduction, private model memorization, and code completion injection→

Cloud IDE Code Generators

Amazon CodeWhispererAWS-specific misconfigurations, overly permissive IAM policies, and insecure S3/Lambda defaults→Gemini Code AssistGCP misconfigurations, insecure Firebase rules, and unauthenticated Cloud Function generation→Jupyter NotebooksCredential exposure in notebook cells, unsafe pickle deserialization, and PII in cell outputs→

AI App Builders & Agents

Replit / v0Full-stack app generation with systemic auth flaws, exposed APIs, and deployment misconfigurations→Devin / Autonomous AgentsUnsupervised code execution, supply chain manipulation, and multi-step vulnerability chains→AI Code Review ToolsReview bypass through obfuscation, false negatives for business logic, and approval manipulation→

By Vulnerability Type (LLM Top 10)

OWASP LLM Security Risks

LLM01: Prompt InjectionBypassing safety rules through malicious prompt engineering and indirect injection→LLM02: Insecure Output HandlingXSS, SQLi, and command injection via unvalidated LLM output→LLM03: Training Data PoisoningBackdoors, biases, and malicious behavior introduced through compromised training data→LLM04: Model Denial of ServiceResource exhaustion attacks targeting LLM inference and agentic workflows→LLM05: Supply Chain VulnerabilitiesCompromised model weights, poisoned fine-tuning datasets, and malicious plugins→LLM06: Sensitive Information DisclosureModels leaking PII, credentials, and proprietary data from training or context→LLM07: Insecure Plugin DesignTool-calling vulnerabilities and unsafe plugin execution in agent frameworks→LLM08: Excessive AgencyLLM agents with overly broad permissions executing unintended actions→LLM09: OverrelianceBlind trust in AI output without validation leading to deployed vulnerabilities→LLM10: Model TheftExtraction, cloning, and reconstruction of proprietary model weights→

By Framework

AI/ML Frameworks

LangChainDeserialization vulnerabilities, tool-calling flaws, and prompt injection in chain orchestration→LlamaIndexRAG pipeline injection and document store poisoning attacks→OpenAI APIAPI key leakage, function-calling argument injection, and token manipulation attacks→

Vulnerability Types

CWE-862

HIGH

Missing Authorization in AI-Generated APIs

AI-generated API endpoints often lack proper authorization checks, allowing any authenticated user to access other users...

CWE-863

HIGH

Incorrect Authorization in AI-Generated Code

AI code assistants implement authorization checks using client-side logic or incorrect comparison patterns that can be e...

CWE-601

HIGH

URL Redirection to Untrusted Site (Open Redirect)

AI-generated redirect logic using user-supplied URLs without validation, enabling phishing attacks by redirecting users ...

CWE-611-AI

HIGH

XML External Entity (XXE) in AI-Generated Parsers

AI assistants generate XML parsing code with default parser settings that allow external entity resolution, enabling fil...

CWE-434

HIGH

Unrestricted Upload of File with Dangerous Type

AI-generated file upload handlers often lack file type validation, size limits, and proper storage configuration, enabli...

CWE-352-AI

HIGH

Cross-Site Request Forgery in AI-Generated Forms

AI assistants generate form handlers without CSRF token validation, enabling attackers to trick authenticated users into...

CWE-326

HIGH

Inadequate Encryption Strength in AI-Generated Code

AI code assistants generate encryption with weak key lengths (DES, 1024-bit RSA), outdated modes (ECB), or insecure rand...

CWE-209

HIGH

Generation of Error Message Containing Sensitive Information

AI-generated error handlers expose stack traces, database schemas, file paths, and internal IP addresses in error respon...

CWE-1321

HIGH

Prototype Pollution in AI-Generated JavaScript

AI assistants generate JavaScript that uses recursive object merge or deep clone without prototype chain protection, ena...

CWE-943

HIGH

NoSQL Injection in AI-Generated Code

AI code assistants generate MongoDB queries using user input directly in query operators ($gt, $ne, $regex), enabling No...

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