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.
Enterprise agent deployments have moved past pilot phase. The security patterns that have survived contact with production look different from the ones the industry was selling a year ago.
A working review of Claude Code's security posture, sandboxing model, and the practical controls enterprises need to deploy it safely at scale.
Real exploits cross package boundaries. Griffin AI's graph follows them; Mythos-class tools often stop at the file they are reading.
AI-BOM is how you describe an AI system's supply chain — models, datasets, prompts, inference environments. Griffin AI ingests it as structured inventory. Mythos-class tools try to talk about AI while remaining blind to the AI systems they describe.
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.
Weight-level tampering leaves cryptographic and statistical fingerprints. Here is what current research says about detecting a modified checkpoint before it reaches inference.
If your agent can execute code, something it reads from the internet can execute code. Pick your sandbox before the agent picks one for you.
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.
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.
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