The verification pipeline
Four stages. Every finding is verified before it reaches your team.
Scan
AI-Native Pattern Detection
Cipher Core ingests your codebase and applies AI-native pattern analysis — not legacy regex signatures. It understands the structural patterns unique to AI-generated code: repetitive scaffolding, hallucinated API calls, inconsistent error handling, and unsafe defaults that LLMs commonly produce.
Languages supported: TypeScript, JavaScript, Python, Go, Rust, Java. Framework-aware analysis for Next.js, Express, Django, FastAPI, and more.
Verify
Exploit Simulation in Isolation
Every finding from the scan phase is passed to Cipher Labs — our exploit verification engine. Labs spins up isolated environments and attempts controlled exploits against each reported vulnerability. If the exploit succeeds, the finding is confirmed. If it fails, the finding is dismissed.
No more triaging hundreds of theoretical alerts. You only see what's real.
Exploit
Evidence-Backed Confirmation
Confirmed vulnerabilities are enriched with exploit evidence: the attack vector, the payload that succeeded, the affected code path, and the CWE/OWASP classification. This is not a probability score — it's a proof of exploitability.
Findings include reproduction steps, severity scoring, and direct links to the vulnerable code.
Fix
Remediation Guidance
Each verified finding ships with specific remediation guidance — not generic advice. Cipher understands the context of your code and suggests precise fixes, including code patches, dependency updates, and configuration changes.
CI/CD integration means fixes can be validated automatically on the next push.
Why traditional AppSec fails
Legacy tools were built for human-written software. AI-generated code requires a fundamentally different approach.
| Category | Traditional | Cipher |
|---|---|---|
| Detection Method | Pattern matching / regex | AI-native reasoning + pattern analysis |
| False Positive Rate | 30–70% industry average | < 3% after exploit verification |
| Verification | Manual triage required | Autonomous exploit simulation |
| AI Code Support | Not designed for LLM output | Purpose-built for AI-generated patterns |
| Pipeline Impact | Minutes to hours | < 2 seconds average scan time |
| Output | Theoretical alerts | Confirmed, evidence-backed findings |
AI-Native by Design
Built for AI-generated code
AI coding agents produce code with distinct patterns: repetitive scaffolding, hallucinated API references, inconsistent validation, unsafe defaults. Cipher's analysis engine is trained to recognize these patterns — not just match against known CVEs.
Pattern Recognition
Identifies structural vulnerabilities unique to LLM-generated output — patterns that legacy SAST tools miss entirely.
Context-Aware Analysis
Understands framework conventions, dependency trees, and runtime behavior — not just isolated code snippets.
Continuous Learning
RAG-powered knowledge base maps findings to OWASP Top 10 and CWE classifications with real-time intelligence.