Mythos
Safeguard articles tagged "Mythos" — guides, analysis, and best practices for software supply chain and application security.
103 articles
SOC 2 Type II Evidence: Griffin AI vs Mythos
A SOC 2 Type II auditor samples a control population across a reporting period. Griffin AI creates that population as a natural output. Mythos-class pure-LLM tools leave you reconstructing it.
Citation Accuracy: Griffin AI vs Mythos
An AI security tool that cites the wrong advisory is worse than one that says nothing. Griffin AI benchmarks citation accuracy at 0.89 similarity; Mythos does not.
SSRF Detection: Griffin AI vs Mythos
Server-side request forgery is a test of how well your scanner understands the boundary between trusted and untrusted URLs. Griffin's engine resolves URL construction through string builders, template engines, and HTTP client configuration; Mythos reads the code and guesses. On modern applications that is the difference between a finding you can ship and a finding you cannot defend.
Cache Hit Optimisation: Griffin AI vs Mythos
Prompt caching and engine memoisation combine to make Griffin AI scans repeat-cheap. Pure-LLM tools recompute the same reasoning on every run.
CWE Classification Accuracy: Griffin AI vs Mythos
Getting the CWE right is not a taxonomic hobby. It drives remediation, compliance mapping, and detection engineering. Here is how grounded and pure-LLM scanners compare.
Data Residency Controls: Griffin AI vs Mythos
Data residency is no longer a procurement checkbox. It is an architectural property that most pure-LLM vendors cannot deliver without major rework.
Regression Testing on Fixes: Griffin AI vs Mythos
A remediation PR is only useful if it does not break anything else. Griffin AI runs targeted regression before opening; Mythos-class tools usually do not.
SPDX Coverage: Griffin AI vs Mythos
SPDX is the format auditors ask for, the format regulators reference, and the format most enterprise procurement teams standardize on. Griffin AI treats it as a first-class graph. Mythos-class tools treat it as a long document.
FedRAMP HIGH Posture: Griffin AI vs Mythos
FedRAMP HIGH demands 421 controls with documented, continuous evidence. Griffin AI produces control-mapped records every day. Mythos-class pure-LLM tools cannot fill a 3PAO evidence package.
Taint Propagation: Griffin AI vs Mythos Approaches
Taint tells you whether attacker data actually reaches a sink. Griffin AI propagates it; Mythos-class tools infer it. The difference shows up fast.
Adversarial Resistance: Griffin AI vs Mythos
Griffin AI reports 98-100% hold rate against adversarial probes. Most Mythos-class tools have never published an adversarial number at all.
Deserialization Vulnerabilities: Griffin AI vs Mythos
Unsafe deserialization looks obvious on a slide and impossible on a real codebase. Sinks are language-specific, gadgets live in third-party libraries, and the tainted byte can arrive wrapped in six layers of framework ceremony. Griffin's engine-plus-LLM design handles each of those concerns separately; Mythos-style pure-LLM scanners blur them into pattern-matching.