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.
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.
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.
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.
Weight-level tampering leaves cryptographic and statistical fingerprints. Here is what current research says about detecting a modified checkpoint before it reaches inference.
An MCP server tells the world what it can do through its capability declaration. Auditing those declarations catches drift, tool poisoning, and misconfiguration before an agent gets talked into using the wrong one.
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.
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.
AI plugins connect LLMs to external services, creating a supply chain of trust that most users never examine. The risks are significant.
Prompt caching and engine memoisation combine to make Griffin AI scans repeat-cheap. Pure-LLM tools recompute the same reasoning on every run.
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