AI browsers can be convenient, but they also come with security risks.

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.

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.

From AI agents and deepfakes to prompt injection, cybersecurity teams are confronting risks that traditional defences were not designed to handle ...

Context bomb cybersecurity research from Tracebit shows that a single hidden text string inside an AWS cloud decoy stopped ...

Agentic AI browsers have opened the door to prompt injection attacks. Prompt injection can steal data or push you to malicious websites. Developers are working on fixes, but you can take steps to stay ...

OpenAI says GPT-Red automates prompt injection testing and helped GPT-5.6 Sol record sixfold fewer direct injection failures than GPT-5.5 in benchmark ...

Awareness of all the ways prompt injection can be effected will help security teams spot a new generation of attacks.

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 ...

A now corrected issue allowed researchers to circumvent Apple’s restrictions and force the on-device LLM to execute attacker-controlled actions. Here’s how they did it. Interestingly, they ...

Attackers have begun embedding hidden instructions in websites to target AI agents, according to new research. Zscaler's ...

A critical prompt injection vulnerability in GitHub Agentic Workflows could allow unauthenticated attackers to leak private repository data, ...