Postdoctoral researcher at FAIR, Meta. Prior in MBZUAI. Obtained PhD from NUS, Singapore.

Paris, France
Joined December 2020
Hongyan Chang retweeted
Paper is at arxiv.org/abs/2609.10104. Thanks, @AliShahinShams1 and co-authors!
🎉 Our paper "Distributed and Private Textual Data Synthesis from Embeddings" has been accepted to Privacy Enhancing Technologies Symposium, @PET_Symposium 2027! Synthetic text is a promising way to learn from user data without exposing it. But ...
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🎉 Our paper "Distributed and Private Textual Data Synthesis from Embeddings" has been accepted to Privacy Enhancing Technologies Symposium, @PET_Symposium 2027! Synthetic text is a promising way to learn from user data without exposing it. But ...
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Hongyan Chang retweeted
Introducing Muse Image and Muse Video, the first media generation models developed by Meta Superintelligence Labs. Muse Image is our most advanced image generation model yet. It follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. It also brings agentic tool use capabilities to image generation and integrates with Muse Spark. You can try Muse Image in the Meta AI app and web, as well as in Instagram Stories and WhatsApp – starting in limited countries with more locations on the way. Today we’re also previewing Muse Video, which is built upon the same pretraining base as Muse Image to deliver exceptional visual fidelity with native audio support. Learn more about both models: go.meta.me/7f4427
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Hongyan Chang retweeted
Muse Image is live! And it comes with a watermark detection tool available at meta.ai/identification, which the watermarking team at FAIR has worked on! Would love your feedback on what you make with it and on the detection tool!
Introducing Muse Image and Muse Video, the first media generation models developed by Meta Superintelligence Labs. Muse Image is our most advanced image generation model yet. It follows instructions faithfully, edits with precision, composes from multiple references, and draws on Instagram for social context. It also brings agentic tool use capabilities to image generation and integrates with Muse Spark. You can try Muse Image in the Meta AI app and web, as well as in Instagram Stories and WhatsApp – starting in limited countries with more locations on the way. Today we’re also previewing Muse Video, which is built upon the same pretraining base as Muse Image to deliver exceptional visual fidelity with native audio support. Learn more about both models: go.meta.me/7f4427
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Hongyan Chang retweeted
1/9 Excited to share TextSeal, our new state-of-the-art watermark for large language models at FAIR / Meta Superintelligence Labs (@AIatMeta) 🔐 Paper: arxiv.org/abs/2605.12456 Code: github.com/facebookresearch/…
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Hongyan Chang retweeted
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.
LiteLLM HAS BEEN COMPROMISED, DO NOT UPDATE. We just discovered that LiteLLM pypi release 1.82.8. It has been compromised, it contains litellm_init.pth with base64 encoded instructions to send all the credentials it can find to remote server + self-replicate. link below
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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.
LLMs are everywhere, but can they pose privacy risks? We introduce a Context-Aware Membership Inference Attack, assessing such privacy risks. Paper: arxiv.org/pdf/2409.13745 @brave blog: brave.com/blog/privacy-in-ll… Authors: @Hongyan_Chang @AliShahinShams1 @minoskt Hamed @rzshokri
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Hongyan Chang retweeted
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!
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Hongyan Chang retweeted
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! 🙏 teobaluta.github.io/
We're thrilled to welcome Teodora Baluta, SCP's new assistant professor! Teodora focuses her research on securing generative #AI systems by analyzing the algorithms and datasets behind them to prevent data misuse or theft. Read more about her here: cc.gatech.edu/news/new-facul…
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LLMs are everywhere, but can they pose privacy risks? We introduce a Context-Aware Membership Inference Attack, assessing such privacy risks. Paper: arxiv.org/pdf/2409.13745 @brave blog: brave.com/blog/privacy-in-ll… Authors: @Hongyan_Chang @AliShahinShams1 @minoskt Hamed @rzshokri
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Hongyan Chang retweeted
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. 🧵
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Intern position at @brave : brave.com/careers/ 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.
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Hongyan Chang retweeted
Watermark Smoothing Attacks against Language Models: arxiv.org/abs/2407.14206. LLM watermarks as statistical perturbations to token probabilities can be removed *without* significantly reducing text quality. Adversary having access to a weaker model can infer and smooth watermarks
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Hongyan Chang retweeted
🥁 Llama3 is out 🥁 8B and 70B models available today. 8k context length. Trained with 15 trillion tokens on a custom-built 24k GPU cluster. Great performance on various benchmarks, with Llam3-8B doing better than Llama2-70B in some cases. More versions are coming over the next few months. llama.meta.com/llama3/
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Hongyan Chang retweeted
Google announces Stealing Part of a Production Language Model We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \20 USD, our attack extracts the entire projection matrix of OpenAI's Ada and Babbage language models. We thereby confirm, for the first time, that these black-box models have a hidden dimension of 1024 and 2048, respectively. We also recover the exact hidden dimension size of the gpt-3.5-turbo model, and estimate it would cost under 2,000 in queries to recover the entire projection matrix. We conclude with potential defenses and mitigations, and discuss the implications of possible future work that could extend our attack.
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Hongyan Chang retweeted
We have released a new version of our ML Privacy Meter auditing tool. It efficiently measures the information leakage of ML algorithms about their training data. Check it out. We use the state of the art algorithms, and provide many examples and tutorials: github.com/privacytrustlab/m…
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Hongyan Chang retweeted
Chang (@Hongyan_Chang) Algorithmic models can suffer if sensitive data, which may be restricted by law, is prevented from use in testing and development. #PrivacyCon21
Hongyan Chang retweeted
When designing trustworthy machine learning algorithms, it is very important to have a clear understanding of the relation and trade-offs between different requirements. We analyze the privacy risks of providing group fairness for ML. arxiv.org/pdf/2011.03731.pdf We show how
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Hongyan Chang retweeted
I wrote a blog post two+ years ago last time folks were discussing bias in data vs bias in algorithms. aaronsadventures.blogspot.co… I think it is helpful to keep toys examples in mind to remember how simple and seemingly unobjectionable choices can lead to undesirable outcomes.
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Some people say that one shouldn't care about publication and the quality matters. However, the job market punishes those who don’t have publications in top ML venues. I empathize with students and newcomers to ML whose good papers are not getting accepted. #ICLR2021 1/
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