AI Security
In-depth guides and analysis on ai security from the Safeguard engineering team.
676 articles
Fine-Tune Drift Measured On Eval Sets
Fine-tuning to improve one task frequently regresses others. Without eval harnesses, the regressions ship. The measurable drift is larger than vendors admit.
Griffin AI vs Gemini On-Device: Developer Tools
Gemini on-device models are fast and cheap. For the developer-tool layer, they're useful. For the engine-plus-LLM layer, on-device is not the right fit.
Grounded Reasoning vs Hallucinated: Griffin AI vs Mythos
The difference between grounded reasoning and hallucinated reasoning is not eloquence — it's citation. A look at how Griffin AI anchors every claim.
MCP Server Capability Drift Detection
MCP servers do not stay still. Tool surfaces drift, scopes expand, and the server you approved is not the server in production. Here is how to catch that.
Prompt Injection From Research To Bug Bounty
Prompt injection started as a research curiosity. In 2026 it is a regular line item on bug bounty leaderboards, with payout norms, scope definitions, and a maturing triage culture.
Guardrail Consolidation: Market Dynamics 2026
Two dozen AI guardrail vendors in 2023. A much smaller set in 2026. The consolidation has pattern — integrated platforms beat standalone guardrails.
Evidence-Attached Fix PRs Reviewers Trust
Reviewers trust fix PRs that come with evidence. Here is how to attach the right evidence so AI-assisted remediation gets approved on the first pass.
Breaking Change Awareness: Griffin AI vs Mythos
An auto-fix that closes a vulnerability and breaks the build is not a fix. Breaking-change awareness separates auto-PRs that ship from auto-PRs that get reverted.
MCP Server Authorization Patterns in 2026
The Model Context Protocol shifted agent integration from custom glue to a standard surface. Authorization patterns that work, and the ones that keep biting teams.
Audit Trail Quality: Griffin AI vs Mythos
An audit trail is only useful if you can answer questions from it. Quality is not about volume — it's about the ability to reconstruct decisions after the fact.
Sanitizer Detection: Griffin AI vs Mythos
A vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
AI-BOM and ML-BOM: The State of Standards in 2026
Where AI-BOM and ML-BOM specifications stand in 2026, which formats have real adoption, and what to capture today even if the standards are still in motion.