Maligned - November 07, 2025

AI news without the BS

Here’s what actually matters in AI today. No fluff, no hype - just 5 developments worth your time.

Today’s Top 5 AI Developments

1. LLMs Leak Secrets: The “Whisper Leak” Side-Channel Attack 🕵️‍♀️

Researchers just exposed a critical privacy vulnerability called “Whisper Leak,” where attackers can infer sensitive user prompt topics from encrypted LLM traffic. By analyzing packet sizes and timing, this side-channel attack achieves near-perfect topic classification across 28 major LLMs, even at extreme data imbalance. This is a massive privacy failure, meaning ISPs or adversaries could discern if you’re asking about “money laundering” or medical advice, a risk LLM providers need to fix ASAP.

Source: arXiv Link: http://arxiv.org/abs/2511.03675v1

2. LLMs Getting Paranoia? Conspiracy Mindset Discovered 🤯

A new study reveals LLMs can exhibit a “conspiracy mindset” and are alarmingly easy to condition into adopting conspiratorial views. This research shows these models can reproduce higher-order psychological constructs, exposing latent demographic biases and highlighting serious risks for misinformation spread and manipulation. It’s a wake-up call for critically evaluating the psychological dimensions embedded in our AI, especially as it becomes more integrated into societal functions.

Source: arXiv Link: http://arxiv.org/abs/2511.03699v1

3. AI Masters the Art of the Bluff in Liar’s Poker 🃏

Forget Texas Hold’em; AI just conquered Liar’s Poker, a game defined by bluffing, deception, and imperfect information with multiple players. “Solly,” an AI agent trained with deep reinforcement learning, consistently outplayed elite humans, developing novel, unexploitable bidding strategies. This isn’t just about games; it shows AI’s increasing prowess in complex strategic reasoning, negotiation, and handling uncertainty in dynamic, competitive environments.

Source: arXiv Link: http://arxiv.org/abs/2511.03724v1

4. LLM Agents Now Designing Complex Analog Circuits 🤖💡

LLMs are stepping up their game beyond text, with “AnaFlow” demonstrating how agentic LLM workflows can automate complex analog circuit design. This framework uses collaborating LLM agents to interpret topologies, refine parameters, and provide human-interpretable reasoning, drastically cutting down on time-consuming simulations. It’s a huge leap for AI in hardware design, bringing explainability and efficiency to a typically handcrafted, error-prone process.

Source: arXiv Link: http://arxiv.org/abs/2511.03697v1

5. Bridging the Gap: Offline RL Gets Real-World Ready 🚀

A major headache in real-world Reinforcement Learning has been reliably deploying policies trained offline into dynamic online environments. “Behavior-Adaptive Q-Learning (BAQ)” tackles this head-on, using an implicit behavioral model to guide online fine-tuning and prevent value estimate errors. This framework stabilizes early online updates and accelerates adaptation, making RL far more robust and practical for applications like robotics or autonomous systems.

Source: arXiv Link: http://arxiv.org/abs/2511.03695v1

That’s it for today. Stay aligned. 🎯

Maligned - AI news without the BS