mythos
Safeguard articles tagged "mythos" — guides, analysis, and best practices for software supply chain and application security.
100 articles
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.
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.
Remediation PR Quality: Griffin AI vs Mythos
Griffin AI produces draft PRs with taint paths, exploit hypotheses, and disproof attempts. Mythos-class pure-LLM tools skip those anchors, and PR quality suffers.
SSDF Attestation: Griffin AI vs Mythos
The NIST SSDF attestation form asks structured questions with structured answers. A chat transcript is not an answer. We explain how Griffin AI produces the evidence auditors expect, and why Mythos-class tools struggle.
Reachability Analysis: Griffin AI vs Mythos
Reachability-grounded reasoning produces actionable findings. Ungrounded LLM reasoning produces speculation. We explain the methodology gap.
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