Mythos finds the bugs. Now ship the fix.
Claude Mythos has shown what frontier models can find in your code. Finding bugs is the simple part. Acting on them is where the complexity lives, and that's still on you and your team.
Discovery is the easy part
Mythos showed what AI can find: thousands of critical security bugs in weeks. Fixing them means understanding what each one touches, and that's still complex work.
AI finding is here
Mythos Preview surfaced 10,000+ high or critical findings across roughly 50 partner orgs in a month. Cloudflare alone: 2,000 bugs, 400 critical, with accuracy exceeding human testers. The discovery side of the problem is crossing over.
Every fix is a decision tree
A critical patch means knowing what it touches: business rules, dependencies, threading, compliance, who depends on the soft-fail behavior. The wrong order breaks the audit trail. The wrong refactor breaks the SLA. This is where remediation lives, and it isn't a speed problem.
The wrong fix ships another bug
Auto-fixes look complete and break things quietly. A generic patch that respects no business rules creates a regression that wasn't there before. The fastest patch is the one that didn't ship a CVE-for-a-CVE.
Patching is an understanding problem
A Mythos finding tells you where the bug is. Acting on it needs the blast radius, the business logic it touches, and the tribal knowledge of what's safe to change. Static analysis alone misses intent. AI alone hallucinates. Senior engineers alone don't scale. We run all three.
Deterministic analysis
Our proprietary engine maps the blast radius of every finding. What depends on what, which callers are direct, which are indirect, which paths touch business logic. The factual base layer your remediation team works from.
AI for scale
Anchored in the blast-radius map, our AI agents extract the constraints, contracts, and rules every patch has to respect. The context your remediation team and your AI tools work from. Indexed, queryable, ground truth.
SMEs and AI experts
Senior engineers validate every patch in context. They catch the cases where the "right fix" breaks a threading model, an SLA, or a compliance rule. The reason your incident response doesn't ship a CVE-for-a-CVE.
Auto-fix introduces a synchronous handshake on the payment hot path. Tested at 50 TPS, fails at 200. Replacing with async validation and a parity test against the production processor contract. Locked.
Six of the twelve callers depend on the old validation returning a soft-fail for staging certs. Patch must keep the staging-mode flag. Surfaced to the agent as rule.security.staging_softfail.
Build the understanding layer
Four stages. Fixed price per stage. Commit one step at a time, with validation evidence at every one.
Assessment
Snapshot of your codebase and remediation readiness. Dependency inventory, third-party surface, patching maturity audit. Scoped plan, risks, and success criteria.
- Codebase snapshot and remediation audit
- Scoped plan
- Risk register
- Locked success criteria
Specification
Extract the business logic, critical flows, and validation rules patches have to respect. Documented, queryable, with parity test specs locked.
- Architecture and dependency maps
- Extracted business logic
- Documented critical flows
- Parity test specifications
Modernization
Build the Knowledge Base. Index the codebase, capture the tribal knowledge, expose it via MCP. The understanding layer your remediation team and AI tools work from.
- Validated Knowledge Base
- Blast-radius analysis per finding
- MCP server and endpoints
- Integrations with your AI tools
Enablement
Keep patching safely as your code evolves. Parity test suite, remediation playbooks, training, and a Knowledge Base your tools and SOC can query, handed off to your team.
- Parity test suite
- Remediation playbooks
- Team training
- Queryable Knowledge Base
A workspace, not a stack of files
Every deliverable lives in one place. Yours to keep, queryable by your tools and your SOC.
Delivered change
- Validated Knowledge Base
- MCP server and endpoints
- Integrations with your AI tools
- Audit-ready context for disclosure
System understanding
- Architecture and dependency maps
- Blast-radius analysis per finding
- Extracted business logic
- Critical flow documentation
- Queryable by MCP
Enablement assets
- Parity test suite
- Remediation playbooks
- Team training
- Queryable Knowledge Base
Get the understanding layer your remediation team will need
Get in touch with our team. We'll talk through your codebase and what an understanding layer for your remediation team and AI tools looks like.
Get in touch