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.
When LLMs hallucinate package names that don't exist, attackers can register them. This supply chain attack vector is already being exploited in the wild.
Engine work parallelises cleanly. Model calls do not. We explain why Griffin AI's throughput scales with CPU while Mythos-class tools bottleneck on rate limits.
Finding a bug is not the same as proving it is exploitable. How Griffin AI synthesises concrete exploit paths and why pure-LLM scanners rarely get past the sketch stage.
DeepSeek Coder has become a favourite for code-focused workloads. This is how it compares to Griffin AI when the job is security review, not code generation.
LLM spend forecasting is where finance teams meet AI engineering for the first time. The patterns that produce predictability are specific.
RAG pipelines have six or seven supply chain surfaces, and most teams are only watching one. Here is how the attacks actually look in production.
Claude Haiku is the cost-efficient model Griffin uses for high-volume scan interpretation. Here's how raw Haiku compares to Haiku inside Griffin's bulk pipeline.
SEvenLLM set out to measure how well LLMs handle Security Event analysis, the unglamorous day-to-day work of SOCs and IR teams. A design review of what the benchmark covers, how it was built, and where the coverage maps or does not map to real operations.
Prompt injection attacks against large language models represent a dangerous new frontier in software supply chain security. Here's what defenders need to know.
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