The AI agent that hunts logic flaws
An AI-powered agent that runs a deep analysis of your SonarQube projects, hunting broken access control, business logic, and authentication flaws that traditional analysis was never built to catch.
Not all vulnerabilities are bugs in your code
Attackers don't need to break your code, they just need to use it the way it wasn't meant to be used.
Privilege escalation hides in plain sight
Nothing in the code looks wrong. A regular user can still end up with admin rights.
Business logic gets abused
Stacked coupons, skipped workflow steps, missing rate limits. None of it looks like a vulnerability to a scanner.
Reviews can't keep pace
AI-generated code ships faster than any human review cycle can cover.
Pentests happen twice a year
Everything shipped in between goes live unchecked until the next engagement.
SAST and Hunter Agent, one platform.
Algorithmic analysis and agentic reasoning find different classes of vulnerability. Running both, closes the gap.
Known & pattern-based vulnerabilities
- Injection and unsafe data flows
- Known CVEs in first-party and open-source dependency code
- Secrets detection
- Runs on every commit, consistent and repeatable
Intent-based vulnerabilities
- Broken access control and IDOR
- Business logic flaws
- Authentication and session gaps
- Deep, full-codebase reasoning about intent
It hunts through playbooks, like a security researcher.
A playbook is a curated, multi-step sequence of specialized security prompts that encodes Sonar's application security expertise. The agent runs them across your whole codebase and works a lead until it can prove an issue is real.
Authorization bypass through user-controlled key
Raised by Hunter Agent
The code is valid. The intent is not: any user can read another tenant's invoice.
1. Hunt
The agent explores the full codebase and follows data and identity flows across files, building hypotheses about where intent and implementation diverge, the way a researcher does on a code audit.
2. Confirm
Every suspected vulnerability is investigated and validated before it surfaces. You get high-precision findings, not a queue of false positives to triage.
3. Explain
Each finding arrives with a severity, a plain account of why it matters, and the exact location, so a developer can act in minutes instead of reverse-engineering it.
4. Integrate
Confirmed findings land in your SonarQube issue list, tagged and ready to triage, assign, and fix in the workflow your team already uses.
One playbook. Three classes of flaw.
CWE lists are representative, not exhaustive. Works with any language supported by SonarQube Cloud.
Broken access control
IDOR, missing or incorrect authorization checks, privilege escalation, sensitive data exposure, and cross-site request forgery.
Business logic flaws
Skipping required workflow steps, abusing repeatable actions, and missing rate limits.
Authentication & sessions
Broken authentication, session fixation, non-expiring sessions, weak password recovery, missing MFA, and brute-force gaps.
Layered security by design.
SAST, SCA, and Hunter Agent win together for the same reason any layered defense does: each catches what the other cannot. Sonar combines them across the Verify stage, so no single check has to carry the whole codebase's trust.
Multilayered verification
Algorithmic verification, a reasoning agent, and your CI pipeline each catch what the others cannot. Layered coverage is dramatically better than any single tool on its own.
Independent and zero trust
The agent that reviews the code is not the one that wrote it. A different method, clear segregation of duties, and findings that are auditable and repeatable.
Inside your workflow
Verification that scales with how fast your team and its agents ship, without adding review headcount. Findings land as SonarQube issues, with nothing new to install.