V1.0New in V1.0 — Adversarial Patch Loop

Ship code that already
survived the attack.

FlawDetector is the AI-native application security platform that finds vulnerabilities, writes the fix, and proves it works — with an autonomous Red Team vs Blue Team loop running on FlawDetector-LLM V1.0.

No credit card · 5-minute setup · In-house test reports included

app.flawdetector.ai/acme/checkout-service/scan/1024

acme / checkout-service · main

Scan #1024 — 1,842 functions analyzed

2

Critical

5

High

11

Medium

17

Patches ready

Findings · ranked by exploitabilityExport report ↗
CriticalSQL injection via unsanitized query param Fix ready
CriticalHard-coded AWS secret in build script Fix ready
HighSSRF in webhook URL validation Fix ready
HighStored XSS in comment renderer Fix ready
MediumMD5 used for password digest Fix ready
LowStack trace leaked in 500 handler
12,400+

Repositories scanned

94.7%

Detection rate, V1.0 benchmark

3.2%

False-positive rate

41s

Median full-repo scan

Trusted by security teams shipping in production

Product walkthrough

From raw repository to sealed release

Three stages, one pipeline. Every screen below is the actual product — not an illustration.

01 · Detect

Your whole repository, read function by function

Connect a repo and FlawDetector chunks it into analyzable units, runs each through FlawDetector-LLM V1.0, and triages every finding by severity, CWE and OWASP category — in under a minute for most codebases.

app.flawdetector.ai/acme/checkout-service/scan/1024

acme / checkout-service · main

Scan #1024 — 1,842 functions analyzed

2

Critical

5

High

11

Medium

17

Patches ready

Findings · ranked by exploitabilityExport report ↗
CriticalSQL injection via unsanitized query param Fix ready
CriticalHard-coded AWS secret in build script Fix ready
HighSSRF in webhook URL validation Fix ready
HighStored XSS in comment renderer Fix ready
MediumMD5 used for password digest Fix ready
LowStack trace leaked in 500 handler

02 · Fix

A patch you can merge, not a ticket you park

Every finding ships with an explanation in plain language and a merge-ready diff. Apply it in one click, or open it as a pull request with full context for your reviewers.

app.flawdetector.ai/findings/FD-2481
CriticalFD-2481 · CWE-89 · OWASP A03Verified exploitable

SQL injection via unsanitized query param

username flows into a raw f-string query. Red Team confirmed extraction of the users table in round 2. The patch below parameterizes the query — verified to kill the exploit in round 3.

api/auth/login.py+2 −2
def login(request):
username = request.POST['username']
query = f"SELECT * FROM users WHERE name = '{username}'"
row = db.execute(query).fetchone()
+ query = "SELECT * FROM users WHERE name = %s"
+ row = db.execute(query, (username,)).fetchone()
if row is None:
raise AuthError('invalid credentials')
Apply patch Open as PR

03 · Prove

Audit-grade reports, every single run

Each scan is stored, diffable against history, and exportable as an audit-grade report — the same evidence pack behind our in-house test reports.

app.flawdetector.ai/reports/2026-Q3-checkout

Security assessment report

acme / checkout-service — 2026 Q3

Protocol FD-TC-01 · 1,000-trial repeated benchmark · evidence pack attached

A

92/100

Findings sealed

35 / 35

Loop rounds

4

Residual risk

Low

1. Scope & methodology
2. Findings & severity distribution
3. Adversarial verification log
4. Repeated-trial performance annex
In-house test criteria · all rulesets passedExport PDF

NEWNew in V1.0

Two AIs. One war room.
Zero standing vulnerabilities.

The Adversarial Patch Loop pits an AI Red Team against an AI Blue Team inside your CI. Red attacks like a professional pentester. Blue patches, hardens, and re-arms. The loop only stops when the exploit stops working.

adversarial-loop · acme/checkout-service
round 3/∞
AI Red TeamAutonomous offense
AI Blue TeamAutonomous defense

standing by…

── round start · target: api/auth ──

AI Red Team

  • Plans multi-step exploit chains like a human pentester
  • Probes injection, auth bypass, SSRF, path traversal
  • Escalates with context from every failed attempt

AI Blue Team

  • Generates the minimal patch that kills the exploit
  • Hardens configs and adds regression guards
  • Re-runs the exact attack to verify the seal

Every round is recorded — attack path, patch diff, verification log — so your team reviews outcomes, not alerts.

Watch the loop on your repo

Core platform

Everything a security team ships with

The complete FlawDetector engine — the foundation the Adversarial Loop is built on.

01

Whole-repo LLM audit

Intelligent chunking walks your entire codebase and analyzes each function in context, not just diff hunks.

02

Severity triage

Critical to Low, mapped to CWE and OWASP Top 10, deduplicated and ranked by exploitability.

03

Merge-ready fixes

Concrete patch diffs with plain-language rationale — apply in one click or open as a PR.

04

CI/CD & CLI

Gate merges in GitHub Actions, GitLab CI or Jenkins. Full-fidelity CLI for local and air-gapped runs.

05

Scan history & drift

Every result stored and diffable. See exactly which release introduced — or sealed — a flaw.

06

Multi-model engine

FlawDetector-LLM V1.0 first, with GPT and Gemini fallbacks — routed per language and finding class.

07

Audit-grade reports

Export Markdown or PDF evidence packs formatted for auditors and customers.

08

Polyglot coverage

Python, JavaScript/TypeScript, Java, Go, C# and more — one pipeline for every service you run.

Real-time monitoring

Watch your attack surface, as it changes

Every push triggers analysis. Findings stream to the dashboard the moment they're confirmed, and alerts reach your team where they already work.

Push-triggered scans

Webhooks kick off analysis on every commit — no scheduling, no stale results.

Live findings feed

Confirmed findings stream in real time with severity, owner and suggested patch attached.

Alerts that route themselves

Critical findings page the on-call; everything else lands in Slack or email digests.

app.flawdetector.ai/monitor/live
Live monitor — 14 services
99.98% watch uptime
Findings sealed / hour▲ 23% vs yesterday
14:32:07CriticalAuth bypass attempt pattern sealed in prod-apiPatched
14:31:52HighNew SSRF vector on push to payments/mainLoop round 1
14:30:11MediumWeak cipher config drift in infra repoFix ready
14:28:45LowVerbose error surface on staging gatewayTriaged
14:27:03HighDependency CVE-2026-1187 reachable pathPatched

FlawDetector-LLM V1.0

Measured. Repeated. Certified-lab ready.

V1.0 performance was measured across 1,000+ repeated trials on public and private vulnerability corpora, with bottleneck analysis feeding directly into the engine roadmap.

94.7%

Detection rate

Across OWASP benchmark suites

3.2%

False positives

After dedup & exploitability ranking

99.1%

Run-to-run consistency

1,000+ repeated trials

2.4×

Throughput roadmap

From bottleneck analysis, V1.1

RULESET

In-house test criteria, built into every scan

Injection, auth bypass, misconfiguration, weak cryptography and more — run multiple vulnerability rulesets in a single pass, every item maintained against our in-house test criteria.

Pricing

Start free. Scale when it proves itself.

Every plan includes the full detection engine and merge-ready fixes.

Starter

$0

For individual developers and open source.

Start free
  • 3 repositories
  • Weekly full-repo scans
  • Severity triage & fix suggestions
  • Community support
Most popular

Team

$49/mo · per seat

For product teams shipping continuously.

Start 14-day trial
  • Unlimited repositories
  • Push-triggered scans & live monitoring
  • Adversarial Red vs Blue patch loop
  • CI/CD gates, Slack alerts, PR fixes
  • Scan history & drift tracking

Enterprise

Custom

For regulated and air-gapped environments.

Talk to sales
  • On-prem / VPC deployment
  • SSO, SCIM & audit logs
  • In-house test reports & report service
  • Dedicated security engineer

Enterprise

Talk to a security engineer, not a form

Tell us about your stack and compliance targets. We respond within one business day — usually with a scan of your public surface already done.

1,000+ repeated-trial verified engine
In-house test reports on request
On-prem / VPC · air-gapped deploys
Team size

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