OpenAI presented details of its AI’s model’s cyberattack on Hugging Face at Black Hat last week. Simon Willison details the timeline. It’s...
A usage policy for Flock license plate reader cameras tells police not to talk about the cameras:
When cops use Flock to arrest someone in Wapello County, Iowa, they don’t want them to know. A usage policy for the automated license plate reader cameras in the county tells police, in no uncertain terms, to keep them a secret: “DO NOT MENTION ALPR USAGE TO THE OCCUPANTS OF THE VEHICLE,” the policy document reads. “DO NOT MENTION ALPR USAGE IN YOUR REPORT OR COMPLAINT UNLESS ABSOLUTELY NECESSARY.”
This reminds me of IMSI-catchers (Stingray was the most popular) a couple of decades ago. Police would go to even more extremes to hide their usage.
I have been thinking a lot about AI and integrity. Part of that is contextual integrity. I recently found two papers on the topic.
“CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs“:
Abstract: Large Language Models (LLMs) increasingly use persistent memory from past interactions to enhance personalization and task performance. However, this memory introduces critical risks when sensitive information is revealed in inappropriate contexts. We present CIMemories, a benchmark for evaluating whether LLMs appropriately control information flow from memory based on task context. CIMemories uses synthetic user profiles with over 100 attributes per user, paired with diverse task contexts in which each attribute may be essential for some tasks but inappropriate for others. Our evaluation reveals that frontier models exhibit up to 69% attribute-level violations (leaking information inappropriately), with lower violation rates often coming at the cost of task utility. Violations accumulate across both tasks and runs: as usage increases from 1 to 40 tasks, GPT-5’s violations rise from 0.1% to 9.6%, reaching 25.1% when the same prompt is executed 5 times, revealing arbitrary and unstable behavior in which models leak different attributes for identical prompts. Privacy-conscious prompting does not solve this—models overgeneralize, sharing everything or nothing rather than making nuanced, context-dependent decisions. These findings reveal fundamental limitations that require contextually aware reasoning capabilities, not just better prompting or scaling.
“Contextual Integrity in LLMs via Reasoning and Reinforcement Learning“:
Abstract: As the era of autonomous agents making decisions on behalf of users unfolds, ensuring contextual integrity (CI)—what is the appropriate information to share while carrying out a certain task—becomes a central question to the field. We posit that CI demands a form of reasoning where the agent needs to reason about the context in which it is operating. To test this, we first prompt LLMs to reason explicitly about CI when deciding what information to disclose. We then extend this approach by developing a reinforcement learning (RL) framework that further instills in models the reasoning necessary to achieve CI. Using a synthetic, automatically created, dataset of only 700 examples but with diverse contexts and information disclosure norms, we show that our method substantially reduces inappropriate information disclosure while maintaining task performance across multiple model sizes and families. Importantly, improvements transfer from this synthetic dataset to established CI benchmarks such as PrivacyLens that has human annotations and evaluates privacy leakage of AI assistants in actions and tool calls.
Criminals are hacking into public Wi-Fi devices—at hotels, conference centers, and so on—around the world and changing their DNS settings. The goal is to redirect users to fake login pages and steal their credentials.
Fascinating video about searching for life undersea. The video basically makes the point that our bright white searchlights are scaring everything away, and that red light is more neutral. That, plus bait to attract sea creatures, is teaching us a lot about what’s going on down there. Lots of footage of giant squid, and speculation about the colossal squid. Worth watching.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
This is a current list of where and when I am scheduled to speak:
The list is maintained on this page.