AI Security
In-depth guides and analysis on ai security from the Safeguard engineering team.
676 articles
Vector Database Poisoning Trend Watch
Vector databases are now central infrastructure for retrieval-augmented AI. The 2026 attack trend targets the index itself, not the model — and most defenders are not watching the right layer.
AI Coding Assistant Data Leakage Paths
AI coding assistants promise productivity but expand the data leakage surface in specific, mappable ways. The paths, the mitigations, and what enterprise policy actually looks like.
Real-World Vs Synthetic Eval Gap In Security
Synthetic eval benchmarks are controllable. Real-world data is messy. The gap between performance on each is usually large, and vendors prefer one over the other for a reason.
Bulk Remediation Of Aged Vulnerability Backlog
Most security teams are sitting on hundreds of stale findings. Here is how to clear an aged vulnerability backlog with bulk remediation that actually merges.
Griffin AI vs Claude Computer Use: Security
Claude's Computer Use lets an agent drive a GUI. For security, this is powerful and dangerous in equal measure. The architecture around it matters.
Cryptography Misuse Detection: Griffin AI vs Mythos
Crypto misuse is not about broken algorithms. It is about misused parameters, missing checks, and the gap between "it compiles" and "it is secure."
AI Agent Tool Confused Deputy Problem in 2026
A senior engineer's take on the confused deputy problem in AI agent tool use, why it keeps reappearing in 2026, and the architectural patterns that actually fix it.
Ensemble LLMs For High-Precision Security Findings
One model's confident answer is a guess. Multiple models agreeing is evidence. Ensemble approaches raise precision for security-critical findings.
Griffin AI vs GPT-5: Compliance Posture
Compliance posture is about what you can prove, not what you can do. GPT-5 has impressive capabilities; Griffin AI is engineered to be defensible.
Hallucinated Security Findings: Measurable Rates
Pure-LLM security analysis hallucinates findings at rates between 20% and 70% depending on the task and model. Grounding is the architectural answer.
Griffin AI vs Gemini for FedRAMP Workflows
Gemini has FedRAMP-authorised deployment options. Griffin AI builds on FedRAMP-aligned infrastructure. The comparison is about what the customer has to build.
False Positive Rates: Griffin AI vs Mythos Benchmarked
Why pure-LLM security products generate false positives that engine-grounded platforms like Griffin AI structurally cannot — with CWEs and real triage data.