// vulnerability discovery
· from $20/moAn uncensored AI
vulnerability scanner.
Off-the-shelf scanners — Snyk, Semgrep, Checkmarx, SonarQube, Veracode — catch the textbook patterns and miss the business-logic class of bug that actually matters.
They run pattern-matching rules at scale, return a flat list of findings (most false positive), and leave the real audit work to humans. The major coding assistants are not better — Claude or ChatGPT can read code, but the moment your prompt mentions vulnerability, exploit, or attack, the content policy refuses or rewrites the analysis into something useless. TartarusAI reads your code like a senior auditor would: it reasons over multi-file flow, identifies the bug class, writes the dynamic test that confirms exploitability, suggests the patch. No content-policy interference. Same enterprise-grade runtime safety guards. The agent treats vulnerability discovery the way every commercial code-audit firm already does: as a senior-engineering discipline that requires reading code carefully, not running regex matches in parallel.
- ✓Zero refusals on offsec work
- ✓Custom-tuned model — not a wrapper
- ✓Runtime guards on by default
- ✓256K context, sub-2s TTFT
refund if it ever refuses · no card on file · crypto-only · cancel any time
❯ scan this codebase for auth bypass, ssrf, race conditions
target: 14k-line node app, signed audit
▎▣ static-analyzed 247 routes
▎+ found 2 IDOR (admin endpoints, missing tenant check)
▎+ found 1 race window (token-refresh path)
▎+ wrote findings.md + suggested patches per file
done.// what it does
What you ship
when nothing refuses.
Source-level static analysis
Authorization gaps, IDOR, SSRF, race conditions, deserialization, prototype pollution, SSTI, business-logic flaws, OAuth / SAML / OIDC quirks, JWT misuse, file-upload sinks, XXE, SQLi / NoSQLi, command injection, path traversal. The agent reasons over multi-file flow, not just regex matches. Particularly strong on the bugs that require understanding how the application is intended to behave.
Dynamic harness writing
Once a candidate is identified, the agent writes the dynamic test that confirms exploitability. Curl chains, custom fuzzers, race-condition harnesses (parallel-request orchestration with timing), browser PoCs (XSS / DOM Clobbering / CSP bypass), authenticated-flow harnesses for IDOR confirmation. Findings ship with working PoCs, not theoretical concerns.
Patch suggestions
Each finding ships with a proposed fix in your codebase's style. The agent reads the surrounding code, infers the conventions, and writes the fix that fits. You merge or reject; the agent does not silently rewrite anything you did not ask for.
Multi-language coverage
JavaScript / TypeScript, Python, Go, Rust, Java, C / C++, PHP, Ruby, C#, Kotlin, Swift, Elixir. Particularly strong on web frameworks (Express, Fastify, Next.js, Django, Flask, FastAPI, Spring Boot, Laravel, Rails, Phoenix) and on the framework-specific bug classes that off-the-shelf scanners miss because they require framework-aware reasoning.
Container + IaC scanning
Dockerfile review, Kubernetes manifest analysis (privilege escalation paths, network policy gaps, secret exposure), Terraform / Pulumi / CloudFormation infrastructure-as-code review, helm chart review, CI / CD pipeline analysis (GitHub Actions, GitLab CI, CircleCI, Buildkite). The agent identifies misconfigurations that compose into exploitable patterns, not just isolated CIS-benchmark deviations.
Audit report generation
Findings → audit-quality report. Severity scoring, CVSS, evidence collection, repro steps, impact statement, suggested remediation, executive summary, technical deep-dive, attestation paragraph for compliance frameworks (SOC 2, ISO 27001, PCI DSS). Cuts code-audit deliverable turnaround in half.
// workflow
A typical audit pass
You start with a codebase, a scope, and a threat model. The agent reads the codebase structure, identifies the high-priority surface (authorization-bearing endpoints, deserialization sinks, multi-tenant data paths, third-party integration points), and proposes an audit plan sized to your engagement budget.
Per finding the workflow is: candidate identification → dynamic confirmation → patch suggestion → severity scoring. The verification gate runs the candidate exploitation harness against your scratch environment; if it does not reproduce, the finding is dropped before it becomes a false positive in your report. You see what compiles and reproduces, not a flat list of regex matches.
For ongoing audit relationships (quarterly review, per-release audit, security-engineering retainer), the agent maintains state across audits. New findings get diffed against historical ones. Patched findings stay patched (regression detection). New surface from new features gets prioritised against your engagement budget.
// comparison
Versus traditional code scanners
Snyk, Semgrep, Checkmarx, SonarQube, Veracode, GitHub CodeQL — they all run pattern-matching rules at scale. They are good at the textbook stuff (SQL injection in raw SQL queries, hardcoded secrets, known vulnerable dependencies) and bad at everything that requires understanding the application. False-positive rate is usually 50-80% on first run; senior auditors spend most of their time triaging the noise.
TartarusAI reasons over the code instead of running rules across it. Lower false-positive rate because every finding has a working PoC. Higher recall on business-logic bugs because the agent thinks about how the app is supposed to behave, not just what its code looks like. Higher friction per scan (the agent reads code; rules just run regex), so the right pattern is "rule-based scanner for coverage, agent for depth on prioritised surface."
The two work well in combination. The rule-based scanner catches the boring textbook bugs; the agent catches the business-logic bugs and the chained vulnerabilities. Most senior code auditors who have tried both prefer to keep both in the workflow — different tools for different parts of the audit.
- ●Pairs with Snyk, Semgrep, Checkmarx, SonarQube, Veracode, GitHub CodeQL, Trivy, kube-bench, terrascan.
- ●Generates Semgrep custom rules, CodeQL custom queries, custom audit harnesses for the bug classes scanners miss.
- ●Outputs are raw findings + audit reports + patches — no SaaS lock-in.
- ●Particularly strong on multi-file flow, framework-specific bug classes, and chained vulnerabilities.
// compliance
Compliance and audit-deliverable workflows
For organisations that need code audits as part of compliance posture (SOC 2 Type 2, ISO 27001, PCI DSS, FedRAMP, HIPAA), the audit deliverable is often more important than the audit itself. The findings exist to satisfy the auditor; the patches exist to satisfy the next audit cycle. TartarusAI generates compliance-flavoured audit reports per finding, with the attestation paragraphs already drafted in the format your auditor expects.
For security-engineering retainer relationships (the consultancy provides ongoing audit instead of point-in-time engagements), the agent maintains state across audits — diff the new release against the previous baseline, identify what changed, focus the audit on the changed surface. Saves the senior auditor from re-reading the unchanged 90% of the codebase.
// questions
What people actually ask.
How is this different from Snyk / Semgrep / Checkmarx?+
Can I scan a private codebase?+
Does it produce false positives?+
Will it write the exploit, not just flag the bug?+
How does it handle huge codebases (10M+ LOC)?+
Can it generate Semgrep / CodeQL custom rules?+
Does it handle infrastructure-as-code review?+
Is it suitable for compliance-driven audits (SOC 2, ISO 27001, PCI DSS)?+
// ready
Stop fighting refusals.
Start shipping the engagement.
One tier covers most engagements at $20/month. If the agent ever refuses, hedges, or returns neutered output on legitimate engagement work, we refund — see the refund policy.
refund if it ever refuses · no card on file · crypto-only