AI Security
Dynamic Dispatch: Griffin AI vs Mythos
Dynamic dispatch hides real exploits behind indirection. Griffin AI models the dispatch; Mythos-class tools guess. That gap changes outcomes.
Feb 6, 20267 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.
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.
DeepSeek Coder has become a favourite for code-focused workloads. This is how it compares to Griffin AI when the job is security review, not code generation.
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.
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.
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