griffin-ai

Griffin AI vs Inflection Pi for Security Assistance

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.

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.

Griffin AI vs DeepSeek Coder for Security Review

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.

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.

Griffin AI vs Claude Haiku for Bulk Scanning

Claude Haiku is the cost-efficient model Griffin uses for high-volume scan interpretation. Here's how raw Haiku compares to Haiku inside Griffin's bulk pipeline.

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.

Griffin AI vs OpenAI o1 for Security Reasoning

Deep reasoning models are transformative for hard logical problems. Security reasoning is only partially a logic problem—the rest is grounding, policy, and workflow.

Griffin AI vs Gemini Long Context for Codebases

Gemini's million-token context window is a genuinely new capability. For security analysis of large codebases, is it enough on its own?

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.