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-89

SQL Injection

AI code assistants frequently generate database queries using string concatenation instead of parameterized queries, cre...

CWE-79

Cross-site Scripting (XSS)

LLMs often generate frontend code that renders user input without sanitization, enabling attackers to inject malicious s...

CWE-798-AI

Hardcoded Credentials in AI-Generated Code

Code assistants frequently embed example API keys, database passwords, and tokens that developers forget to replace, exp...

CWE-918

Server-Side Request Forgery (SSRF)

AI-generated HTTP client code often lacks URL validation, allowing attackers to make the server fetch internal resources...

CWE-502

Deserialization of Untrusted Data

LLMs generate deserialization code (pickle, Java ObjectInputStream, JSON.parse with revivers) without input validation, ...

CWE-22

Path Traversal

AI-generated file handling code often fails to sanitize file paths, allowing attackers to read or write arbitrary files ...

CWE-330

Use of Insufficiently Random Values

Code assistants generate Math.random() or weak PRNGs for security-sensitive operations like token generation, session ID...

CWE-78-AI

OS Command Injection in AI-Generated Code

LLMs generate shell commands using string interpolation with user-controlled variables, enabling arbitrary command execu...

CWE-94-AI

Code Injection via eval() in AI-Generated Code

AI assistants frequently use eval(), exec(), or Function() constructor with dynamic input, creating code injection vulne...

CWE-287-AI

Broken Authentication in AI-Generated Code

LLMs generate authentication logic with flawed comparison operators (== instead of timing-safe compare), missing rate l...

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