1/9 Excited to share TextSeal, our new state-of-the-art watermark for large language models at FAIR / Meta Superintelligence Labs (@AIatMeta) 🔐
Paper: https://t.co/XMJ1nYrEQF
Code: https://t.co/PAZygG69me
Software horror: litellm PyPI supply chain attack.
Simple `pip install litellm` was enough to exfiltrate SSH keys, AWS/GCP/Azure creds, Kubernetes configs, git credentials, env vars (all your API keys), shell history, crypto wallets, SSL private keys, CI/CD secrets, database passwords.
LiteLLM itself has 97 million downloads per month which is already terrible, but much worse, the contagion spreads to any project that depends on litellm. For example, if you did `pip install dspy` (which depended on litellm>=1.64.0), you'd also be pwnd. Same for any other large project that depended on litellm.
Afaict the poisoned version was up for only less than ~1 hour. The attack had a bug which led to its discovery - Callum McMahon was using an MCP plugin inside Cursor that pulled in litellm as a transitive dependency. When litellm 1.82.8 installed, their machine ran out of RAM and crashed. So if the attacker didn't vibe code this attack it could have been undetected for many days or weeks.
Supply chain attacks like this are basically the scariest thing imaginable in modern software. Every time you install any depedency you could be pulling in a poisoned package anywhere deep inside its entire depedency tree. This is especially risky with large projects that might have lots and lots of dependencies. The credentials that do get stolen in each attack can then be used to take over more accounts and compromise more packages.
Classical software engineering would have you believe that dependencies are good (we're building pyramids from bricks), but imo this has to be re-evaluated, and it's why I've been so growingly averse to them, preferring to use LLMs to "yoink" functionality when it's simple enough and possible.
I’m excited to share that I’ve joined Meta FAIR Paris as a postdoctoral researcher 🎉
We’ve just open-sourced Meta Seal, a production-ready invisible watermarking suite for image, video, audio, and text, released under the MIT license. Please check out👇
We're thrilled to share the open source release of Meta Seal, a comprehensive, SOTA, and MIT-licensed suite of AI watermarking research, models, & training code.
Learn more in the 🧵 below and explore the artifacts here:
https://t.co/yNqmMJWQ9z
Today at #EMNLP2025 (Suzhou, China): @Hongyan_Chang presents CAMIA—Context-Aware Membership Inference Attacks against pre-trained LLMs. Room A104–105. Learn how to assess memorization & info leakage in LLMs.
Excited to present two papers at #EMNLP2025 tomorrow 🎉
🧩 Poster: “Watermark Smoothing Attacks against LMs” — 12:30–13:30, Hall C3
🧠 Oral: “Context-Aware Membership Inference Attacks” — 17:00, Room A104-105
Come chat about LLM safety & privacy!
I'm recruiting students for fall 2026 thru @LTIatCMU & @CMU_EPP, in:
1. Privacy & security of LLMs, coding, long horizon & embodied agents (robotics)
2. Tiny local llms
3. AI for scientific reasoning, esp. chemistry
4. Latent reasoning
5. anything YOU are passionate about!
I am recruiting PhD students!🐝 If you are interested in ML security and privacy, intersected with formal methods and rigorous frameworks, drop me an email!
Retweets are greatly appreciated! 🙏
https://t.co/PyVfRGtCrD
Excited to share that my internship project has been accepted at EMNLP! Grateful for the amazing experience working with @AliShahinShams1 and @minoskt in @Brave.
LLMs are everywhere, but can they pose privacy risks?
We introduce a Context-Aware Membership Inference Attack, assessing such privacy risks.
Paper: https://t.co/2REmFeAmKN
@brave blog: https://t.co/3UOzE153Jp
Authors: @Hongyan_Chang@AliShahinShams1@minoskt Hamed @rzshokri
LLMs are everywhere, but can they pose privacy risks?
We introduce a Context-Aware Membership Inference Attack, assessing such privacy risks.
Paper: https://t.co/2REmFeAmKN
@brave blog: https://t.co/3UOzE153Jp
Authors: @Hongyan_Chang@AliShahinShams1@minoskt Hamed @rzshokri
LLMs can memorize and leak sensitive training data, posing serious privacy risks.
Brave researchers have developed a new open-source method to detect this memorization and information leakage. 🧵
LLMs can memorize and leak sensitive training data, posing serious privacy risks.
Brave researchers have developed a new open-source method to detect this memorization and information leakage. 🧵
@AliShahinShams1 is a really cool mentor. Working with him is as fun as traveling in London. If you are interested in privacy and fairness in machine learning, please check it out.
Intern position at @brave : https://t.co/vdfPWPJIQP
My team is looking for strong students interested in private, secure and trustworthy ML.
Feel free to email me with the subject line "Brave Internship 2025" and highlight your 3 most significant publications on these topics.
Intern position at @brave : https://t.co/vdfPWPJIQP
My team is looking for strong students interested in private, secure and trustworthy ML.
Feel free to email me with the subject line "Brave Internship 2025" and highlight your 3 most significant publications on these topics.