frontier-models
Safeguard articles tagged "frontier-models" — guides, analysis, and best practices for software supply chain and application security.
47 articles
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.
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.
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.
AI-BOM Becoming Mandatory: Regulatory Trend
AI bills of materials moved from voluntary best practice to regulatory requirement in 2026. Multiple jurisdictions now require disclosure of model, data, and component lineage for high-impact AI systems.
Frontier Model Pricing Pressure: Architectural Response
Frontier model pricing is rising even as cheaper alternatives proliferate. The 2026 architectural response is multi-tier model routing — and the security implications are non-trivial.
Context Window Limits: Griffin AI vs Mythos
Context-window size matters less than context quality. A look at how Griffin AI's engine-grounded context beats pure-LLM retrieval at monorepo scale.
Model Substitution Risk In Enterprise Deployments
The model you think you're calling might not be the model that returns. Model substitution is a quiet supply chain risk that deserves explicit controls.
Open-Source LLM Supply Chain Incidents 2026
Open-source LLM ecosystems hit a turning point in 2026 as supply chain incidents — backdoored fine-tunes, compromised weights, malicious adapter packages — moved from rare to recurring.
Why Engine-Plus-LLM Beats Pure-LLM: Griffin vs Mythos
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
Retrieval Context Poisoning At Scale
Retrieval context poisoning scales differently than direct prompt injection. The attacker's leverage grows with the RAG ingest surface.
Griffin AI vs Mythos: Architecture Deep Dive
An architectural comparison of Griffin AI's engine-grounded reasoning stack against the pure-LLM pattern that Mythos-class products rely on.
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