griffin-ai

Auto-Fix Compile Rates: Griffin AI vs Mythos

Griffin AI's auto-fixes compile clean 73 percent of the time and pass with minor edits 87 percent. Mythos-class pure-LLM patches rarely show those numbers for a reason.

EU CRA Readiness: Griffin AI vs Mythos

The EU Cyber Resilience Act wants mandatory vulnerability handling, SBOM delivery, and documented due diligence. Griffin AI produces those artifacts continuously. Mythos-class tools produce conversations about them.

Griffin AI vs OpenAI Codex for Security

Codex-style coding agents are powerful for writing features. Security remediation needs a different shape of system—one that grounds frontier reasoning in SBOM, policy, and reachability context.

Griffin AI vs Gemini Ultra for Security Reasoning

Gemini Ultra sets a high bar on complex reasoning benchmarks. But security reasoning is not benchmark reasoning. Here's how Griffin AI's engine-first approach changes the outcome.

CycloneDX Support: Griffin AI vs Mythos

CycloneDX is not a text format to be summarized — it's a typed graph with dozens of semantically-rich fields. Griffin AI consumes it as a graph. Mythos-class tools consume it as tokens. That difference decides every downstream finding.

Call Graph Depth Compared: Griffin AI vs Mythos

Shallow call graphs miss real exploits; deep graphs surface them. We examine how Griffin AI and Mythos-class tools differ on depth, and why it matters.

Eval Methodology: Griffin AI vs Mythos

A benchmark number is only as good as the methodology that produced it. Here is how Griffin AI builds its harness and why most Mythos-class tools cannot be audited.

SQL Injection Chains: Griffin AI vs Mythos

SQL injection stopped being a single-line bug years ago. Modern chains stitch a tainted parameter through ORMs, caches, background jobs, and downstream services. Griffin AI's engine-plus-LLM architecture follows the taint across those hops; Mythos-class pure-LLM scanners summarise one file at a time and lose the thread.

Hypothesis Quality: Griffin AI vs Mythos

Two AI bug hunters can both generate hypotheses. Only one can defend them. A field study of grounded versus ungrounded hypothesis generation in zero-day discovery.

Air-Gapped Environments: Griffin AI vs Mythos

Air-gapped AI is not a feature flag. It is an architectural commitment, and it separates serious enterprise products from consumer-grade assistants.

Per-Scan Token Cost: Griffin AI vs Mythos

Tiered models and a deterministic engine cut token consumption to the moments that need reasoning. Pure-LLM tools pay full price for every trivial check.

Griffin AI vs xAI Grok for Security