The agentic code security system that guides your AI coding agents to generate safer code, uncover complex vulnerabilities across files and execution paths, and deliver verified fixes.
human prompt
Implement a checkout endpoint so customers can apply a coupon code at checkout.
The AI agent now has the security context needed to avoid potential vulnerabilities.
Our agentic security system builds a holistic context model of your software, uses it to steer AI coding agents away from vulnerable code, uncovers subtle security issues, and verifies proposed fixes.
Understand
Our agentic system builds a holistic contextual model of how your software is supposed to work, and how it could be attacked.
It maps repository structure and data flows, extracts the application’s business rules and threat model, and connects them with relevant vulnerability intelligence.
These benchmarks measure two parts of the workflow: reducing vulnerable code during cod generation and identifying security issues in existing code
Guardrail impact
Percentage of vulnerable generated code [1].
Claude Code with Reware produced 19.22 percent vulnerable generated code. Claude Code with Wiz rules produced 38.24 percent. Claude Code alone produced 42 percent.
Claude Code + Reware
Proactive guardrails applied during AI code generation
0.00%
Claude Code + Wiz rules
Baseline rule guidance from existing security policies
0.00%
Claude Code
AI-generated code without proactive Reware guardrails
0.00%
[1] Vero, Mark, et al. BaxBench: Can LLMs generate correct and secure backends?, 2025.
Detection quality
Measured on an internal benchmark covering a wide range of vulnerability types.
Reware scored 0.96 F1. CodeQL scored 0.63. Semgrep scored 0.45. SonarQube scored 0.31.
0.00
Reware
0.00
CodeQL
0.00
Semgrep
0.00
SonarQube
Supported by
