mythos
Safeguard articles tagged "mythos" — guides, analysis, and best practices for software supply chain and application security.
100 articles
ISO 27001 Mapping: Griffin AI vs Mythos
ISO 27001 Annex A has 93 controls in the 2022 revision, each needing documented evidence. Griffin AI emits records that map cleanly. Mythos-class pure-LLM tools force control owners to narrate.
Refusal Rate Analysis: Griffin AI vs Mythos
A security AI that refuses too often is useless. One that refuses too rarely is dangerous. Griffin AI publishes calibrated refusal benchmarks; Mythos does not.
Path Traversal: Griffin AI vs Mythos
Path traversal is the vulnerability class that punishes lazy analysis. Framework-specific path normalisation, OS-dependent separators, symbolic link resolution, and archive extraction all hide exploitable gaps behind code that looks defensive. Griffin's engine resolves path operations with actual semantics; Mythos reads the variable name and calls it a day.
Exploit Path Synthesis: Griffin AI vs Mythos
Finding a bug is not the same as proving it is exploitable. How Griffin AI synthesises concrete exploit paths and why pure-LLM scanners rarely get past the sketch stage.
Throughput At Scale: Griffin AI vs Mythos
Engine work parallelises cleanly. Model calls do not. We explain why Griffin AI's throughput scales with CPU while Mythos-class tools bottleneck on rate limits.
Audit Log Completeness: Griffin AI vs Mythos
Audit logs are where enterprise AI either proves its seriousness or exposes its improvisation. The gap between Griffin AI and Mythos-class products is visible in the first day of a real audit.
Human Review Burden: Griffin AI vs Mythos
Auto-remediation only scales if human review stays cheap. Griffin AI's grounded PRs keep reviewer time low; Mythos-class PRs push the cost back to humans.
Cross-Package Analysis: Griffin AI vs Mythos
Real exploits cross package boundaries. Griffin AI's graph follows them; Mythos-class tools often stop at the file they are reading.
AI-BOM Awareness: Griffin AI vs Mythos
AI-BOM is how you describe an AI system's supply chain — models, datasets, prompts, inference environments. Griffin AI ingests it as structured inventory. Mythos-class tools try to talk about AI while remaining blind to the AI systems they describe.
SOC 2 Type II Evidence: Griffin AI vs Mythos
A SOC 2 Type II auditor samples a control population across a reporting period. Griffin AI creates that population as a natural output. Mythos-class pure-LLM tools leave you reconstructing it.
SSRF Detection: Griffin AI vs Mythos
Server-side request forgery is a test of how well your scanner understands the boundary between trusted and untrusted URLs. Griffin's engine resolves URL construction through string builders, template engines, and HTTP client configuration; Mythos reads the code and guesses. On modern applications that is the difference between a finding you can ship and a finding you cannot defend.
Citation Accuracy: Griffin AI vs Mythos
An AI security tool that cites the wrong advisory is worse than one that says nothing. Griffin AI benchmarks citation accuracy at 0.89 similarity; Mythos does not.
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