The Challenge
One in three AI-generated pieces of code contains a vulnerability . As development teams increasingly rely on AI coding assistants, security risks are being introduced at an unprecedented rate.
Common Pain Points & How ZeroPath Solves Them
| Pain Point | How ZeroPath Solves It |
|---|---|
| LLM assistants embed unsafe patterns Hard-coded secrets, unsanitized inputs, and broken authentication | Context-aware SAST with AI models Pinpoints flaws and proposes fixes pulled from your code style |
| Developers over-trust the assistant Skip review assuming AI-generated code is secure | Inline VCS integration GitHub/GitLab/Bitbucket checks gate insecure PRs and auto-open a patch MR |
| Traditional scanners drown you in false positives Noise makes it impossible to find real issues | AI-powered accuracy Only actionable alerts reach your PR, each with a one-click fix |
How it Works
1. Detect
AI-aware detections + deep-flow analysis scan every push in < 60s
2. Prioritize
Risk-rank by exploitability, data sensitivity & business logic
3. Fix
LLM generates a ready-to-merge patch and unit test
4. Verify
CI reruns the scan to guarantee the vulnerability is gone
Key Capabilities
AI-Specific Vulnerability Detection
- Prompt injection attacks - Detect when user input can manipulate AI behavior
- Insecure output handling - Catch unvalidated AI responses before they cause XSS or injection
- Training data poisoning - Identify potential data corruption vectors
- Model denial-of-service - Find resource exhaustion vulnerabilities
- Sensitive information disclosure - Prevent AI from leaking confidential data
Business Logic Detection
ZeroPath understands cross-file and cross-repository data flow to catch:
- Faulty authentication and authorization logic
- Broken Object Level Access (BOLA) vulnerabilities
- Complex race conditions and state management issues
One-Click Autofix
- Autogenerated patches that match your coding style
- Natural language modification capabilities
- Ready-to-merge pull requests with unit tests