Mythos Readiness

Autonomous AI AppSec Engineer

Not a scanner. Not a dashboard. A full-time security engineer that never sleeps.

Role Brief

Your AppSec team spends 80% of their time triaging noise.
The AI AppSec Engineer eliminates that entirely.

Traditional SAST/SCA tools flood your queue with thousands of alerts — the vast majority false positives. Mythos Readiness deploys an autonomous engineer that triages the entire funnel, generates exploit proofs, synthesizes patches, and opens PRs. Without a Jira ticket. Without a human in the loop.

Triages 2,847 alerts

Proves 7 real exploits

Opens patches as PRs

Re-scans after every commit

First run completes in < 1 hour

The Surface Area Reduction

Visually, this pipeline acts as a series of concentric filters, stripping away noise and theoretical risk until only verified, actionable patches remain.

Hypothetical Attack Surface (Total Codebase)

Millions of Lines

SAST + Code Review Alerts

Thousands of Alerts

Agentic Triage (Reachability)

Dozens of Paths

Exploit Proof Generation

Confirmed Exploits

Remediate

VERIFIED PATCH

1 Pull Request

Configure your deployment

Three questions, answered instantly

Adjust frequency and depth to see exactly what you get — and what it costs.

How accurate is it?0%

How much noise remains?

What does it cost?$0/mo

Mythos Composable Validation Engine

live

SAST / SCARaw scanner inputs

AI DiscoveryAttack surface map

Hypothesis EngineCorrelate & Predict

Agentic TriageReachability Check

Exploit GenDeterministic PoC

Verified RiskZero False Positives

Validation Frequency

How often the pipeline re-validates your attack surface.

Scheduled

ManualNightlyPer-Commit

Reasoning Depth

Computational mandate granted to the triage agents.

Deep Logic Path

ReachabilityContextualExploit Gen

First hour: full-repo ingestion

A human AppSec engineer spends their first week reading code. The AI AppSec Engineer does it in under an hour. It ingests every scanner in your CI pipeline — Semgrep, Snyk, CodeQL, Checkov, Grype — normalizes all findings into a unified schema, and maps your entire attack surface before anyone opens Slack.

Unified Finding Schema — normalized from all scanners

{
  "id": "f-2847",
  "title": "CWE-89: SQL Injection",
  "severity": "critical",
  "source": "semgrep_sast",
  "type": "sast",
  "sast_file_path": "src/controllers/UserController.ts",
  "repo": "acme/backend-api",
  "vuln_identifier": "vuln_abc123"
}

Simultaneously, the Business Logic Analyzer extracts every exposed endpoint — HTTP method, route, identified threats, and a plain-English summary of what the endpoint actually does. This becomes the attack surface map.

POST

/api/users/import

CSV Injection → SQLi

Critical

GET

/api/admin/export

IDOR via unvalidated param

High

PUT

/api/settings/webhook

SSRF via URL param

High

Every day: triage the entire funnel

Every day — on every new commit — the AI AppSec Engineer runs the Mythos Readiness pipeline. It takes the raw scanner output and systematically eliminates noise until only deterministically proven exploitable vulnerabilities remain. This is the funnel:

Elimination Funnel

From 2,847 alerts to 1 proven exploit

2,847Raw Findings

SAST / SCA Ingestion

Semgrep · Snyk · CodeQL · Checkov · Grype

{ title, severity, source, sast_file_path, type }

−95.5%

127Exposed Endpoints

Attack Surface Mapping

[POST] /api/auth/login · identified_threats[] · severity

{ method, endpoint, identified_threats, business_logic_summary }

−66.1%

43Attack Hypotheses

Threat Hypothesis Generation

sequence_diagram + threat_matrix → asset, threats[], controls[]

{ asset, threats[], existing_controls[], recommendations[], priority }

−83.7%

7Confirmed Exploitable

AI Triage (LangGraph Orchestrator)

Plan → Execute → Evaluate → Synthesize · confidence_score /10

{ triage_status, triage_conclusion, confidence_score, workflow_id }

−85.7%

1Deterministic Proof

Proof of Exploit

Control-flow graph proving the exploit path is reachable via control-flow proof

99.96% NOISE ELIMINATED

What it handles vs. what it escalates

Like any good engineer, it doesn't escalate everything. It handles the 95% autonomously and escalates the 5% that genuinely requires human judgment.

Handles autonomously

Suppresses false positives with proof
Generates threat hypotheses per endpoint
Runs PoC exploit simulations in sandboxes
Synthesizes patches via AI code generation
Opens pull requests with tests included
Re-scans after patch to confirm fix

Escalates to humans

Confirmed exploitable findings (proven, not guessed)
Patches that require architectural decisions
0-day vulnerabilities with no known fix
Business logic flaws requiring product input

What success looks like: The Mythos Funnel

The output of a human AppSec team is a Jira backlog. The output of the AI AppSec Engineer is a deterministically proven exploit — a deterministic control-flow proof that an attacker can traverse from an entry point to a vulnerable sink. Not a heuristic. Not a CVSS score. A proof.

2,804

False Positive

proven safe

Suppressed

low confidence

Exploitable

proven path

For each confirmed exploitable finding, the AI AppSec Engineer generates a Proof of Exploit — a control-flow graph tracing the exact path from attacker-controllable input, through each intermediate function, past any bypassed controls, into the vulnerable sink.

Proof of Exploit: Control-Flow Graph

CVE-2024-XXXX — SQL Injection via CSV Import

Entrypoint

POST /api/users/import

Controller

UserController.importBulk()

Input Parser

csvParser.parse(req.body)

Sanitizer

validateEmail() — checks format only

BYPASSED

Safe Path

"name" field is NOT validated

✓

Data Sink

db.query(`INSERT INTO users (name) VALUES ('${row.name}')`)

EXPLOIT

SQLi via CSV injection in 'name' column

Normal flow

Safe path (dead end)

Exploit path (reachable)

Bypassed control

Interactive Sandbox

Experiment with the parameters that govern the Software Comprehension Boundary. Adjust codebase complexity, AI-generated code ratio, dependency trust depth, runtime correlation strength, and reasoning depth to see where hidden execution risk collapses and verified software understanding emerges.

System Operators

Codebase Complexity60%

Interconnectedness, dependency depth, architectural entropy, service coupling

Reachable Execution Paths2.5M

Total traversable execution paths mapped in the repository

AI-Generated Code Ratio40%

Proportion of machine-generated logic requiring semantic verification

Dependency Trust Depth50%

Transitive dependency exposure, supply-chain opacity, inherited execution risk

Runtime Correlation Strength50%

How well static reasoning aligns with observed execution reality

Reasoning Depth (X)83%

How deeply TuringMind infers, connects, validates, and simulates execution behavior

0% Shallow100% Full Cognition

Software Understanding vs Hidden Risk

Verified software understanding vs. hidden risk density at each reasoning depth.

Comprehension

Risk Density

How to Read This

The white curve shows Verified Software Understanding. The clay curve shows Hidden Risk Density. Where they cross is the 53% Cognitive Comprehension Boundary — the reasoning depth at which the codebase becomes cognitively mapped and hidden execution risk struggles to remain latent.

Cognition Status

Cognitive Integrity

System Comprehension

86%

2,144,108 of 2.5M paths

Hidden Risk Density

18%

Opaque execution surface

Causal Integrity

Cognitive Integrity

Understanding state

Unmapped Surface

~0.36M

Paths outside reasoning boundaries

Simulate Postures:

How you onboard it

Unlike a human hire, there's no 90-day ramp. The AI AppSec Engineer is fully operational the moment it finishes indexing your repository.

Connect your repo

GitHub, GitLab, or Bitbucket. The Gobbler pipeline ingests it into the Semantic Graph Index.

Configure your scanners

Point Semgrep, Snyk, CodeQL at TuringMind. All findings are normalized automatically.

Set your thresholds

Define what gets escalated vs. handled autonomously via .turingmind.yml.

It starts working

The first Mythos Readiness run completes within the hour. The funnel is live.

The org chart

Every capability the AI AppSec Engineer uses is built on a composable, 9-layer architecture. Each layer is independently runnable — this is the "org chart" of the agent's skills, not a monolith.

Platform Architecture

AI Agent Data Structural Composable Runs

Source Code

GitHub

GitLab

Bitbucket

Infrastructure

Kubernetes

Terraform

SBOM

Pkg Graph

Deps

SAST

Semgrep

CodeQL

SCA

CVE

Reach.

Runtime

eBPF

Docker

Code Context

Call Graph

Data Flow

Threat Intel

CVE Intel

TTPs

Memory

FP History

Tribal

Graph Engine

Code

Infra

Identity

Reachability

Runtime

Input

Agents

Analysis

Corr.

PoC Gen

HTTP

API

Sandbox

Isolated

Ephemeral

Safety

Policy

Approval

FP Elimination

Context

Historical

Prioritization

Exploit.

Business

Patches

AI Synth

Upgrade

PR Auto

Auto PR

Tests

Controls

WAF

Flags

Patch Check

Re-scan

Retest

Regression

Unit

Integ.

Endpoint

macOS

eBPF

AI Gov

Perms

Limits

Contain

Isolate

Quarantine

Repo Intel

Scanners

Enrich

Reason

Exploit

Triage