Zero-day risk in third-party AI model dependencies
Malicious Hugging Face models, a trojanized Ultralytics PyPI release, and a torch.load bypass show AI model dependencies now carry real zero-day risk.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Malicious Hugging Face models, a trojanized Ultralytics PyPI release, and a torch.load bypass show AI model dependencies now carry real zero-day risk.
Sonatype's new guide reframes AI dependency risk, but its scanner-based model can't govern agents that install packages and call MCP tools on their own. Here's the gap and how to close it.
45% of AI-generated code fails security tests. Here's how CWE Top 25 weaknesses like XSS, SQLi, and broken auth map to specific LLM coding habits.
Sonatype flags bad packages after the fact. Here's what AI governance for software development requires, and how Safeguard tracks models and output together.
Most of your organization is already using AI you never approved. Here is how to discover it, govern it, and offer sanctioned alternatives before it becomes a breach.
What is an AI Bill of Materials (AIBOM), why do SBOM tools like Sonatype fall short on AI components, and how do teams build one in 2025.
Running LLMs on local hardware eliminates some risks and introduces others. A clear-eyed look at the enterprise risk profile of on-premise and on-device model deployments.
Gartner expects most successful attacks on AI agents through 2029 to exploit access control, with prompt injection as the delivery mechanism. Here is why that single failure mode dominates agentic AI security, and what actually moves the needle.
Claude Code can read your repo, run commands, and edit files — which is exactly why it needs the same security engineering as any privileged developer tool. Here's a practical hardening guide.
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