Read the latest insights on AI security technologies, industry trends, and prompt engineering from the AIM Intelligence research and engineering teams.
Explore how EIA, AdvWeb, and WIPI attack methods exploit vulnerabilities in VLM-powered web agents, revealing serious security concerns for AI systems that interact with web environments.
Explore cutting-edge methodologies for identifying and mitigating vulnerabilities in VLM-powered web agents, including the AdvWeb attack framework and BrowserART red teaming toolkit.
For existing VLM Safety benchmarks, there are cases where the text alone is sufficiently informative without the image. We explore base query generation and toxicity measurement methods.
This week, we held a productive meeting with the KAIST lab to refine the direction of our ongoing research project and to solidify our experimental design. The focus was on integrating psychological approaches with LLMs to design jailbreak prompts.
To evaluate VLM Safety, it is essential to develop a secure model that incorporates the unique characteristics of VLMs. We analyze Figstep and RTVLM datasets to assess typographic visual prompt attacks.
