ai-security
Safeguard articles tagged "ai-security" — guides, analysis, and best practices for software supply chain and application security.
593 articles
Prompt-injection vectors specific to MCP servers and how to layer defenses
MCP servers expose three distinct prompt-injection surfaces — resource contents, tool outputs, and sampling requests — and each one needs its own defense layer. Here is how to think about them together.
Prompt injection attacks against AI coding/security tools
AI coding assistants like Copilot and Cursor can be hijacked by hidden text in files, comments, and packages. Here's how prompt injection malware works and how Safeguard detects it.
What Is Prompt Engineering? A Security Guide for LLM Applications
Prompt engineering is how you steer an LLM, and it is also where a lot of application security now lives. Here is how to write prompts that resist injection and leakage.
Artificial Intelligence Security Tools: What They Do and How to Choose
Artificial intelligence security tools now sit in two camps: tools that use AI to defend software, and tools that defend the AI itself. Knowing which one you need shapes the whole buying decision.
Security scanning for MCP servers and AI agent tool use
MCP servers give AI agents direct tool access, but most ship unvetted. Here's how security scanning catches tool poisoning and rug-pull attacks.
AI Information Security: How to Protect Data in AI Systems
AI information security is the practice of protecting the data that flows through AI systems, training sets, prompts, outputs, and the models themselves, from disclosure, poisoning, and misuse. Here is a working model of the risks and controls.
AI Cybersecurity Tools and Solutions: The 2026 Landscape
AI cybersecurity tools in 2026 split into three real categories — AI-augmented detection, AI-specific application security, and autonomous response — and most vendors only actually cover one.
What Is an AI Accelerator, and What Are Its Security Risks?
An AI accelerator is hardware built to speed up machine learning math. Once you offload models onto one, the security work shifts to the software and data around it.
AI Coding Assistant Security: 2026 Buyer Comparison
A security-focused buyer comparison of AI coding assistants in 2026: code quality risk, data exfiltration controls, license exposure, and policy enforcement.
The State of Agentic AI Adoption report
New survey data on the state of agentic AI adoption shows enterprises racing to deploy autonomous agents faster than security teams can govern them.
AI Trust Report: developer sentiment on AI-generated code
Safeguard's 2026 AI Trust Report surveyed 1,412 developers and finds 91% use AI coding tools weekly, but only 34% trust the code it produces.
5 risks of open source software in 2026
Open source now makes up most enterprise code. Here are 5 risks defining open source software security in 2026 — and how to close the exploitability gap.
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