AI code-security platform that reviews changes, validates vulnerabilities and proposes fixes.
Hacktron is an AI-native application-security platform focused on finding exploitable vulnerabilities before code ships. Its current PR Review product connects to GitHub, GitLab and Bitbucket, indexes the broader codebase rather than only the changed diff, traces attacker-relevant paths and reports findings directly inside pull or merge requests.
The product distinguishes itself from generic code review by centering exploitability. Hacktron says findings are accompanied by a working proof of concept and that issues without demonstrated impact are not surfaced. Developers can ask Hacktron to propose fixes, while triage feedback and repository-level rules can be used to teach the system project-specific context and reduce repeated false positives.
Current workflow integrations include Slack for triage, Jira and Linear for ticket creation, and MCP/REST access for tools such as Cursor, Claude Code and Codex. Automations can verify, remediate and retest matching findings and then notify teams. Hacktron also offers white-box pentesting that combines repository access, threat modelling, taint tracing and dynamic validation to produce audit-oriented reports.
The documentation describes the older Workbench/CLI/IDE-extension surface as deprecated, so the current review is scoped to the web platform, PR Review, API/MCP and white-box pentest surfaces rather than legacy developer tooling.
The current Pro plan is $40 per developer per month and includes 50 pull requests per developer, with unlimited scans per PR and $1 for each additional PR. A 14-day free trial is offered without a credit card, and Hacktron states that open-source projects can use the product free.
Enterprise pricing is custom. The enterprise tier adds custom rules per repository, custom security-tool integrations, SSO, audit logs, role-based access controls and 24/7 priority support with a dedicated account team.
This pricing is easy to understand but can scale rapidly for large engineering organizations. The correct value comparison is not against a consumer coding assistant but against application-security tooling and manual review time. Public evidence is not yet sufficient to quantify avoided incidents or analyst-hours independently.
Hacktron publishes unusually concrete technical claims: whole-codebase indexing, exploit-backed findings, automated remediation and white-box dynamic validation. It also maintains a public vulnerability/advisory surface, including a 2026 BeyondTrust pre-authentication RCE disclosure, which is a useful signal that the team performs real offensive-security research.
The privacy documentation says Hacktron is SOC 2 compliant with a Type 1 report audited by Insight Assurance. It also states that OpenAI, Anthropic and Google are used as model providers under zero-data-retention agreements and are not permitted to train on Hacktron data. These are first-party compliance statements and enterprise buyers should verify the current report and contractual terms directly.
External validation remains early. A May 2026 company-distributed funding announcement reported a $2.9M pre-seed round and about $240K in revenue over the first nine months, plus early security work for companies including Perplexity and Supabase. Product-launch coverage shows early community attention, but neither funding nor launch traction proves vulnerability-detection accuracy.
Findings24 and other product directories corroborate the current codebase-aware PR-review positioning, inline findings and repository-rule workflow. They do not provide controlled comparisons against SAST, SCA or competing AI security products. No mature G2/Capterra/TrustRadius corpus or independent benchmark measuring precision, recall, false positives, false negatives or exploit-validation success was found in the reviewed source set.
The reviewed evidence does not support a defensible external numeric product rating. Missing evidence is treated as unknown rather than zero.
Hacktron is best suited to software teams that already ship through modern pull-request workflows and need application-security review embedded before merge. It is especially relevant where security staff are scarce and engineering teams want findings expressed as concrete exploit paths rather than long generic vulnerability lists.
It is less suitable for organizations that primarily need infrastructure, endpoint or network security rather than application code review, and for buyers that require a long independent benchmark history before adopting a security control. Large teams should model seat count and PR overage economics before rollout.
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