AI Security
In-depth guides and analysis on ai security from the Safeguard engineering team.
676 articles
Why LLMs Are Structurally Insecure (and What That Means for Your Pipeline)
Language models are not insecure because of a bug you can patch. They are insecure by construction — non-deterministic, context-poisonable, and unreproducible. Here is how to reason about them without pretending otherwise.
Auto-PR Remediation Without Broken Builds
Automated fix pull requests sound great until half of them fail CI. Here is how to ship auto-PR remediation that keeps the green build, every time.
Supply Chain Risks of AI Coding Assistants
Copilot, Cursor, and Claude Code change what enters your codebase and how. A practitioner's map of the real supply chain risks — hallucinated packages, rules-file injection, and unreviewed transitive trust.
DSPM for AI: navigating data and AI compliance regulations
DSPM for AI closes the gap traditional tools miss: tracking sensitive data through embeddings, fine-tuning, and vector stores to meet EU AI Act and Colorado AI Act requirements.
Anthropic's Mythos Vulnerability Scanner: An Honest Assessment of Strengths, Weaknesses, and Reasons to Be Cautious
Anthropic's Mythos model is generating buzz for AI-powered vulnerability detection. We break down what it does well, where it struggles, and why security teams should approach the results with healthy skepticism.
The Limits of Single-Model Vulnerability Scanning: A Technical Analysis of the Mythos Approach
Anthropic's Mythos model claims to find vulnerabilities in open-source code using a single LLM. We analyze where this approach falls short and why production-grade zero-day discovery requires Safeguard's Multi-Agent TAOR Deep Think AI Engine.
API Surface Reviewed: Griffin AI vs Mythos
Most platform comparisons stop at features. The API surface is where automation and integration actually happen — and where vendors quietly diverge.
Why LLM-Based Vulnerability Scanning Needs More Than a Single Model
Large language models are being used to find vulnerabilities in open-source code. But a single model, no matter how capable, isn't enough. Here's why multi-agent orchestration, structured CWE analysis, and deep context matter more than model size.
Launching Zero-Day Discovery: How Safeguard's Multi-Agent TAOR Deep Think AI Engine Finds Vulnerabilities Before Anyone Else
Safeguard launches its Zero-Day Discovery Engine, powered by the Multi-Agent TAOR Deep Think AI Engine — a multi-lead, multi-sub-agent architecture that performs deep CWE analysis on open-source packages to uncover vulnerabilities that traditional scanners miss.
Zero-Day Discovery In Your Dependency Graph
Most zero-days that hurt enterprises in 2026 live three or four hops deep in the dependency graph. Here is what it takes to actually find them there.
OWASP Top 10 for LLM Applications, Explained
A practitioner's walkthrough of the OWASP Top 10 for LLM Applications: what each risk looks like in a real system, which ones bite first, and the mitigations that hold up.
Claude Code and AI Coding Agent Security Basics
Anthropic Claude Code security rests on permission gating, sandboxed execution, and human approval for risky actions — the same fundamentals any AI coding agent needs before it's allowed to run commands or edit code unattended.