AI Security
Novel Bug Class Detection: Griffin AI vs Mythos
What happens when the bug does not match any known CWE? A study of how grounded and pure-LLM scanners perform on genuinely novel vulnerability patterns.
Feb 11, 20266 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
What happens when the bug does not match any known CWE? A study of how grounded and pure-LLM scanners perform on genuinely novel vulnerability patterns.
A senior engineer's side-by-side look at Griffin AI and Mythos — why engine-grounded reasoning beats pure-LLM security intuition when the audit clock starts.
An AI that reads your security data needs the same access controls as a human analyst. Most pure-LLM vendors stop at the role name. Safeguard enforces the scope.
A remediation PR explanation is either evidence or storytelling. Griffin AI attaches taint paths and disproof attempts; Mythos-class tools attach plausible prose.
Dynamic dispatch hides real exploits behind indirection. Griffin AI models the dispatch; Mythos-class tools guess. That gap changes outcomes.
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.
VEX is how you turn a vulnerability list into an actionable work queue. Griffin AI ingests VEX documents as structured statements that filter findings at policy time. Mythos-class tools read them as advisory prose and lose the filtering entirely.
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.
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.
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