Charles J Yeo
PulseAugur coverage of Charles J Yeo — every cluster mentioning Charles J Yeo across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
-
Fundamental flaw makes LLMs vulnerable to unfixable attacks, researchers say
Researchers have identified a fundamental flaw in large language models that makes them vulnerable to attacks, potentially rendering them unfixable. This vulnerability allows malicious actors to trick LLMs into revealin…
-
Fundamental LLM Flaw Exposes Models to Unsolvable Security Vulnerabilities
Researchers have identified a fundamental flaw in large language models (LLMs) that makes them highly vulnerable to attacks, potentially undermining their safety and reliability. This vulnerability, demonstrated at the …
-
AI Models Vulnerable to Chain-of-Thought Spoofing
Researchers have identified a vulnerability in AI Large Language Models (LLMs) where they struggle to differentiate between instruction sources. This "Chain-of-Thought Spoofing" technique exploits the models' reasoning …
-
AI exploit tricks chatbots into sharing harmful info by faking reasoning
AI researchers have discovered a new exploit called 'CoT Forgery' that tricks large language models into divulging harmful information, such as how to synthesize cocaine. This exploit works by embedding fabricated reaso…
-
LLM safety rules bypassed by exploiting role confusion, study finds
A new paper titled "Prompt Injection as Role Confusion" by Charles Ye, Jasmine Cui, and Dylan Hadfield-Menell explores a vulnerability in large language models (LLMs) where safety rules can be bypassed through role impe…
-
Prompt injection exploits LLM role confusion, new research finds · 8 sources tracked
New research indicates that prompt injection attacks exploit a fundamental flaw in how large language models perceive roles, rather than a lack of safety filters. Researchers found that models prioritize the stylistic p…
-
AI role confusion enables 60% success rate for prompt injection attacks
Researchers have identified prompt injection in large language models as a consequence of "role confusion," where models mistake injected text for legitimate input due to its perceived origin rather than its labeled rol…