AI Security
In-depth guides and analysis on ai security from the Safeguard engineering team.
779 articles
Llama 4 Release and LlamaFirewall: A Defender's Guide
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.
MCP Server Authentication and Authorization: Securing the AI Tool Layer
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 Agent Tool Calling Security: Risks and Mitigations
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 Agent Frameworks: A Security Assessment of the New Autonomous Frontier
AI agents that can execute code, browse the web, and manage infrastructure are proliferating. The security implications of these autonomous frameworks demand scrutiny.
LLM-Augmented Bug Discovery Methodology
A practitioner's methodology for using LLMs to augment — not replace — traditional bug discovery workflows, with patterns that hold up under real review load.
MCP Protocol Security: What the Model Context Protocol Means for Supply Chains
Anthropic's Model Context Protocol standardizes how AI models interact with external tools. The security implications for software supply chains are significant.
nullifAI: Broken Pickles and the Hugging Face Detection Gap
ReversingLabs disclosed two malicious Hugging Face models that evaded Picklescan by using broken 7z-packed PyTorch archives. We unpack the technique.
Prompt Injection as a Supply Chain Risk: When AI Dependencies Are Exploitable
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.
Security Testing for LLM-Powered Applications
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.
Deepfakes and Social Engineering: The Human Layer of Supply Chain Attacks
AI-generated deepfakes are making social engineering attacks against software supply chains more convincing and harder to detect.
AI Model Poisoning: Detection Techniques for the Software Supply Chain
Poisoned AI models are a supply chain threat that traditional security tools can't detect. Here are the emerging techniques for identifying compromised models.
AI Code Review for Security: How Effective Is It Really?
AI-powered code review tools promise to catch vulnerabilities faster than humans. We tested the claims against reality.
Self-healing security runs on Safeguard.
Your first fix PR is minutes away.
No sales call required, even your agent can complete the purchase over MCP.