frontier models
Safeguard articles tagged "frontier models" — guides, analysis, and best practices for software supply chain and application security.
47 articles
Safeguard Now Supports Every Major AI Model Family for Zero-Day Discovery: Anthropic, OpenAI, Gemini, Microsoft, Meta, and Your Own Models
You should not have to choose between your organization's AI strategy and your security platform. Safeguard's agentic zero-day discovery and remediation pipeline now works on Anthropic Claude Fable 5, OpenAI GPT, Google Gemini, Microsoft Phi, Meta Llama, Safeguard native models, and privately hosted custom models — all running as first-class agents in the same Multi-Agent TAOR Deep Think AI Engine.
CAISI's May 2026 Frontier Model Testing Agreements: Pre-Deployment Evaluation Becomes a Supply-Chain Control
On May 5, 2026, NIST's CAISI signed pre-deployment evaluation agreements with Google DeepMind, Microsoft, and xAI, bringing five frontier labs into a government testing program covering cyber, bio, and chemical risk.
AI Agent Supply Chain Attacks: 2026 Trend Watch
AI agents pull tools, models, and data from a sprawling chain of upstream providers. In 2026 attackers learned to poison that chain — and the fallout is shaping how enterprises buy and operate agentic systems.
Agentic AI Budget Explosions And Cost Controls
Agent runaway is no longer a theoretical risk — it is a line item on quarterly variance reports. The 2026 trend in agentic AI is less about model capability and more about who pays when an agent loops.
MCP Vulnerability Disclosure Trends In 2026
MCP servers went from a niche protocol to standard agent infrastructure in under two years. The vulnerability disclosure landscape is catching up — fast, messily, and with patterns worth tracking.
AI Coding Assistant Data Leak Incidents Trend
AI coding assistants are now standard developer tooling. The incident data from 2025 and early 2026 shows a recurring pattern of source code, credential, and customer data leaking through them.
Scaling Across Repos: Griffin AI vs Mythos
Multi-repo security reasoning is a graph problem, not a retrieval problem. How Griffin AI's engine scales where pure-LLM products flatten into guesswork.
Model Substitution Attacks: An Emerging Pattern
An attacker who can swap the model behind an API call can read every prompt and shape every response. The emerging trend in 2026 is model substitution as an attack class with its own techniques and disclosures.
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.
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.
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.
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.