Mythos finds vulnerabilities in code that already shipped.
Acutis prevents them from shipping in the first place.
Mythos and Acutis sit at opposite ends of the software lifecycle. Mythos is reactive: it scans existing codebases to discover vulnerabilities that have already been deployed.[1] Acutis is preventive: it formally verifies AI-generated code before it enters the codebase. One finds the fire, the other prevents it.
Mythos is Anthropic's unreleased frontier model, restricted to approximately 40 partner organizations under Project Glasswing.[1] A single discovery campaign costs approximately $20,000 in compute.[3] Acutis runs locally, verifies in microseconds, and costs nothing per scan.
Anthropic's own data shows individual discovery campaigns costing ~$20,000, with single model runs under $50 each.[3] But you don't know which run will hit, so you pay for the full campaign. Acutis verifies in 0.034ms with zero compute cost beyond the local machine.
Mythos's capabilities are frozen at training time. Improving its coverage of new vulnerability patterns or new libraries requires new model versions on Anthropic's release timeline, not yours.[1] Acutis needs nothing. The AI coding assistant already knows the new library and declares the security semantics in the contract. The verifier doesn't change.
Mythos is available to approximately 40 organizations through Project Glasswing.[1] Acutis is an open MCP server that any AI coding assistant can integrate with today. No partner consortium, no waitlist, no restricted access.
| Acutis | Mythos | |
|---|---|---|
| Purpose | Prevent vulnerabilities in AI-generated code | Find vulnerabilities in existing codebases |
| When it runs | At code generation time, before commit | Post-deployment, on existing codebases[1] |
| Analysis method | Property lattice — formal taint verification | Frontier LLM — agentic code reasoning |
| Guarantee type | Mathematical — deterministic, reproducible | Probabilistic — model-dependent, non-reproducible |
| Cost per scan | $0 — runs locally in 0.034ms | ~$20,000 per discovery campaign[3] |
| New library/framework | Zero changes — AI declares semantics per invocation | Capabilities frozen at training time; improving coverage requires new model versions[1] |
| Availability | Open MCP server — install today | Restricted to ~40 partner organizations[1] |
| Trust model | Zero trust — unknown = dangerous, BLOCK by default | Model confidence — findings depend on model reasoning |
| Output | ALLOW / BLOCK with property flow traces and proof artifacts | Vulnerability reports with severity assessments |
| CWE coverage | CWE-79, CWE-89 (extensible by design) | Broad — memory safety, logic flaws, injection, and more |
| Language support | Python, JavaScript | Any language the model can reason about |
To be fair.
Mythos can scan millions of lines of existing code and find vulnerabilities that have been hiding for decades, including a 27-year-old bug in OpenBSD and a 16-year-old bug in FFmpeg.[2] Acutis verifies code at the point of generation. It doesn't scan your existing codebase.
Mythos has found memory safety bugs, logic flaws, kernel vulnerabilities, and browser sandbox escapes.[2] Acutis currently covers injection CWEs (CWE-79, CWE-89) with an extensible architecture for adding more.
Mythos doesn't just find bugs; it constructs working exploits to prove impact. Acutis produces formal proof artifacts showing why code is safe or unsafe, but doesn't generate exploits.
Mythos cleans up the past. Acutis secures the future. Use Mythos (or similar scanning tools) to find vulnerabilities in your existing codebase. Use Acutis to make sure your AI coding assistants don't introduce new ones. The scanning market is already crowded (CrowdStrike, Palo Alto Networks, and others are all Glasswing partners).[1] Verifying AI-generated code at the point of generation is greenfield.