Researchers say the technique can manipulate how vision-language models interpret both images and user prompts.

One malicious prompt gets blocked, while ten prompts get through. That gap defines the difference between passing benchmarks and withstanding real-world attacks — and it's a gap most enterprises don't ...

Traditional attacks try to break into systems, but model poisoning changes how systems behave after they are trusted.

As threat actors increase their attacks on large language models, securing enterprise AI against growing attacks has become a critical challenge for cybersecurity professionals. According to a recent ...