Runs inside your environment. 6 scanner engines plus AI cross-file reasoning — ranked findings, exploit paths, and patches. Source code never transmitted.
16 languages · 13 frameworks · 3 clouds · 6 IaC · 7 package managers
Krita normalizes output from every scanner, deduplicates by SHA-256 fingerprint, then runs two AI layers — per-file deep scan and cross-file reasoning — before a single alert reaches your team.
Every finding below comes from a real scan of a public repository. No synthetic data, no hypotheticals.
The node-serialize library's unserialize function allows deserialization of functions, enabling arbitrary code execution.
Use JSON.parse() for structured data. Avoid libraries that allow function deserialization.
The JWT secret is set to a common dictionary word that can be cracked in milliseconds using Hashcat or John the Ripper.
Use a cryptographically strong random string of at least 32 characters stored in a secure environment variable.
Cross-file reasoning identified that multiple API and GraphQL endpoints lack the authenticateToken middleware, exposing them to unauthenticated access.
Apply authenticateToken middleware to all routes except explicit public endpoints.
General-purpose models apply generic reasoning to security problems and get generic results. KritSecLLM is purpose-built for AppSec — trained on CVE databases, exploit chains, OWASP taxonomy, and real-world breach patterns — so it understands authorization boundaries, data flows, and business logic the way your security team does.
Scan, triage, assign, remediate, and report — without stitching together five tools.
Per-file vulnerability analysis with full AI enrichment — exploit scoring, CWE/OWASP mapping, and AI-generated patches. Two reasoning layers run in sequence.
Traces execution paths across modules, middleware, and services. Finds IDOR, BOLA, and auth gaps that per-file analysis misses.
Severity-based management with enforced SLAs — 4h CRITICAL, 24h HIGH, 72h MEDIUM. Auto-escalation and merge blocking on breach.
Every finding auto-tagged to CWE, OWASP, CVSS, NIST 800-53, ASVS, and PCI DSS. Evidence artifacts generated for SOC 2, ISO 27001, HIPAA.
Industry-standard SARIF export for GitHub Advanced Security, GitLab Security Dashboard, and Azure DevOps. Inline PR comments on every scan.
Findings flow into your existing tools automatically — no manual export, no copy-paste.
Air-gapped by default in both modes. Source code never leaves your environment.
The problems below aren't edge cases — they're the daily reality for every security engineer. Here's what we do about each one.
78% of scanner alerts are false positives. Engineers spend days triaging noise instead of fixing real issues.
AI enrichment validates every finding against your actual execution paths before it reaches your queue. Only real issues surface.
Business logic flaws — IDOR, BOLA, privilege escalation — are invisible to pattern matchers. They ship completely undetected.
Krita builds a cross-file model of your authorization boundaries and ownership rules, then checks every route against them.
6 point tools, 6 different formats. Aggregating findings is a full-time job before any fixing can even start.
One pipeline ingests 8 attack surfaces and normalises every finding into a unified schema with CWE, OWASP, CVSS, and AI-generated fixes.
A finding says 'SQL injection at line 47'. It doesn't say why it's exploitable in your architecture, or how to actually fix it.
Every finding exits with an exploit path, a CVSS vector, an AI-written patch, and a task with an SLA — ready to assign in 45 minutes.
of critical bug bounty findings are auth and business logic flaws — invisible to every scanner
false positive reduction when AI verifies execution paths before surfacing alerts
lines of source code ever leave your environment — only normalized findings cross the boundary
Connect a repo. Get ranked findings with AI-generated patches, assigned tasks, and blocked merges — in one pipeline.
No agents to install. No new infrastructure. Connect to GitHub or GitLab, and the pipeline runs in your existing CI environment. Your source code never leaves your network.
Every finding comes with an AI-generated patch, exploit path, and compliance mapping — ready to assign in 45 minutes.
From SQL injection to privilege escalation, from leaked secrets to cloud misconfigs — 8 attack surfaces covered with deterministic scanners and AI-enriched reasoning.
────────────────────────────────────[CRIT] CWE-89 auth/login.ts:47SQL injection via user_input → cursor.execute()Exploit score: 9.1 | CVSS: 9.8Fix: Parameterize query, add @validate_input[HIGH] CWE-79 components/Comment.tsx:112Reflected XSS in renderComment()Exploit score: 7.6 | CVSS: 6.5Fix: Sanitize output, use textContent[OK] 5 findings · 2 patches · tasks created────────────────────────────────────
Half of critical bug bounty findings are authorization and business logic flaws — invisible to every rule database ever written. Krita uses AI to reason about your system, not just pattern-match against it.
Trained exclusively on security corpora — CVE databases, exploit chains, OWASP taxonomy, real-world breach patterns. Ships as the default reasoning layer. No prompt-engineering required, no hallucinated exploits.
Model-agnostic by design. Swap in any provider for any reasoning task — no vendor lock-in, no data leaving your environment unless you choose it.
The highest blast radius flaw class. A single missing check means an attacker doesn't need to exploit anything else — they just walk in. AI traces every permission gate, session mechanism, and JWT claim across all routes.
IDOR, BOLA, privilege escalation. No rule database covers these — they require understanding what your application is supposed to do, then finding where it doesn't enforce that. Only AI with system-level context can reason about them.
Auth defined in service A, missing in service B. Input sanitized in the route handler, bypassed three layers deep. Cross-file reasoning maps the entire execution path — not just individual functions.
Vibe-coded vulnerabilities: hallucinated imports, insecure LLM defaults, copy-paste credential leaks. Purpose-built to catch the new class of flaws that ship when developers trust LLM output without review.
Source code never leaves your environment. The scan engine runs in your CI/CD or infrastructure. Only normalized finding JSON crosses the boundary.
Krita reasons across your codebase — catching business logic flaws, cross-file vulnerabilities, and authorization gaps that pattern matchers miss. Inside your environment.
Every finding auto-mapped to CWE, OWASP, and 43 compliance frameworks. Audit trail built in.
AI-generated patches in 45 min. Per-file rescan to verify fixes. 8 attack surfaces in one pipeline.
43 frameworks from CIS to SOC 2. Evidence links, severity SLA tracking, structured audit logging.
Webhook triggers, SARIF export, Jira/Slack/Teams integration, CI/CD YAML templates. Zero manual gates.
SAST, SCA, secrets, IaC, cloud, network, runtime, and business logic — one scan, one report, one CI/CD integration.
Multi-project scoping, per-client isolation, exportable SARIF, white-label ready.
CWE, OWASP, CVSS, NIST, PCI DSS — auto-tagged on every finding. Role-based access, encryption, audit logging, and 43 compliance frameworks built in.
user_input flows from the HTTP request body directly into cursor.execute(query) without parameterisation or escaping. An attacker can exfiltrate the full database with a single crafted request.
Request early access and run a scan inside your own environment. Your source code never leaves.