Reviews every PR and MR
Finds exploitable issues before they are merged.
PR / MR REVIEW
AI code review that catches real, exploitable vulnerabilities.
Hacktron is an AI code reviewer built for security. It reads every pull request and merge request with full codebase context, finds exploitable vulnerabilities, and gives engineers a fix inside GitHub and GitLab. Less noise and more depth than traditional SAST.
No credit card required
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Uses codebase context
Indexes repositories and call graphs instead of only reading the diff.
Closes fixed findings
Detects remediation commits and resolves stale alerts automatically.
HOW IT WORKS
Security feedback where developers already work.
Findings are posted on the vulnerable lines in GitHub or GitLab, so the review stays attached to the code that introduced the risk.
Integrate GitHub or GitLab
Connect GitHub or GitLab and choose the repos Hacktron should review.
Review PRs and MRs
Hacktron posts inline findings with the context needed to reproduce and fix the issue.
Tune the signal
Triage comments and .hacktron/rules.md teach Hacktron what matters in your app.
Hacktron focuses on real, exploitable security issues with clear impact and remediation guidance.
It consistently uncovers high-value findings in PRs before they are published, helping us spend less time triaging and more time fixing the real risk.
Feedback loop
Fewer false positives after every review.
A finding that matters in a payments flow may be noise in an internal tool. Hacktron learns from your triage comments and adapts to the threat model of each codebase.
Auto-resolution
Fixed issues do not linger in the backlog.
When the next commit patches a vulnerability, Hacktron recognizes the fix and closes the finding without waiting for manual cleanup.
Project context
Rules for the parts only your team knows.
Use .hacktron/rules.md to describe auth patterns, trusted sources, ignored paths, and conventions unique to your application.
WORKFLOW
Send findings to the tools that already own remediation.
Pipe findings into Slack for visibility and Linear for tracking, so security work does not disappear into a dashboard nobody checks.
COVERAGE
One reviewer for all of application security
Hacktron Review is built for web, mobile, backend, API, CLI, and native codebases. It focuses on exploitable behavior, not long lists of low value alerts.
Business logic flaws
SQLi, XSS, SSRF, XXE
Prompt injection
Memory safety bugs
Auth and access control
Infrastructure-as-code exposures
Supply-chain risk
Secrets and credentials
For teams currently relying on traditional automated security tools, trying Hacktron is an easy decision.
In our experience, it surfaces issues that other providers simply miss and does so with a level of speed and precision that is hard to match.
SECURITY VS GENERAL REVIEW
A general AI reviewer will still let an authorization bug ship.
General AI code reviewers like CodeRabbit and Greptile grade style, readability, and maintainability. That feedback is useful, but it is not security. Hacktron reviews for exploitability instead.
Hacktron
Traces how data moves through your codebase with call graphs, then asks whether an attacker could actually reach and abuse a code path.
- Broken access control
- Injection through indirect sinks
- Race conditions in payment flows
- Prompt injection in LLM features
General AI code reviewers
Comment on how the code is written, not what an attacker can do with it. Security remarks compete with a stream of style and quality feedback.
- Naming and style conventions
- Readability and structure
- Maintainability suggestions
- Security mixed into general feedback
Every finding ships with a proof-of-concept and an AI fix prompt, so the question is never whether the issue is real. It is how fast the fix can merge.
FAQ
Frequently asked questions.
A quick rundown of how Hacktron Review fits into pull request and merge request security workflows.
Is this just a SAST scanner?
No. Hacktron Review uses repository context and call graphs to reason about exploitability, not only syntax patterns. With advanced AI reasoning, it can find business logic flaws and other types of vulnerabilities that traditional SAST scanners miss.
Where do findings appear?
Findings appear as inline pull request or merge request comments, with enough context for engineers to reproduce and fix the issue. Every finding comes with a proof-of-concept exploit, and an AI prompt that can be used to fix the issue.
How does the reviewer improve over time?
Triage comments and project rules become feedback. When developers and security engineers leave comments on findings, Hacktron learns what is urgent, irrelevant, trusted, or intentionally ignored for each codebase.
Put Hacktron on the next pull request or merge request.
Start with a free trial or book time to walk through your repositories and review workflow.
No credit card required