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.
Meta shipped Llama 4 Scout and Maverick on April 5, 2025, along with Llama Guard 4, LlamaFirewall, and CyberSecEval 4. We unpack what defenders should deploy and what to ignore.
The Model Context Protocol enables AI agents to interact with external tools and data sources. Securing MCP servers requires authentication, authorization, and input validation patterns specific to the AI agent context.
AI agents that call tools -- APIs, databases, file systems, code interpreters -- convert non-deterministic LLM output into real-world actions. Securing this boundary is the defining challenge of agentic AI.
AI agents that can execute code, browse the web, and manage infrastructure are proliferating. The security implications of these autonomous frameworks demand scrutiny.
A practitioner's methodology for using LLMs to augment — not replace — traditional bug discovery workflows, with patterns that hold up under real review load.
Anthropic's Model Context Protocol standardizes how AI models interact with external tools. The security implications for software supply chains are significant.
ReversingLabs disclosed two malicious Hugging Face models that evaded Picklescan by using broken 7z-packed PyTorch archives. We unpack the technique.
Prompt injection is not just an application vulnerability. When LLMs process content from the software supply chain -- package descriptions, README files, commit messages -- injection becomes a supply chain attack vector.
Applications built on large language models introduce novel attack surfaces that traditional security testing does not cover. This guide addresses the specific testing methodologies needed for LLM applications.
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