To prevent prompt injection attacks when working with untrusted sources, Google DeepMind researchers have proposed CaMeL, a defense layer around LLMs that blocks malicious inputs by extracting the ...

Malicious web prompts can weaponize AI without your input. Indirect prompt injection is now a top LLM security risk. Don't treat AI chatbots as fully secure or all-knowing. Artificial intelligence (AI ...

Researchers have discovered two vulnerabilities in the widely used Cursor AI-enabled integrated development environment (IDE) ...

Emily Long is a freelance writer based in Salt Lake City. After graduating from Duke University, she spent several years reporting on the federal workforce for Government Executive, a publication of ...

Security leaders must adapt large language model controls such as input validation, output filtering and least-privilege access for artificial intelligence systems to prevent prompt injection attacks.

In the AI world, a vulnerability called a “prompt injection” has haunted developers since chatbots went mainstream in 2022. Despite numerous attempts to solve this fundamental vulnerability—the ...

Prompt injection and supply chain vulnerabilities remain the main LLM vulnerabilities but as the technology evolves new risks come to light including system prompt leakage and misinformation.

Your LLM-based systems are at risk of being attacked to access business data, gain personal advantage, or exploit tools to the same ends. Everything you put in the system prompt is public data.

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Invisible prompts once tricked AI like old SEO hacks. Here’s how LLMs filter hidden commands and protect against manipulation. For a brief moment, hiding prompt injections in HTML, CSS, or metadata ...