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Prateek Mittal

Prateek Mittal has 5 papers in our library, in 2023–2026, most of them on Bias, fairness & safety, Language models & pretraining, Vision & language; often with Peter Henderson, Xiangyu Qi, Tinghao Xie.

5 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 43 · 225 works · 14,133 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 700 times in our library · 10 more in our library without a page yet · ⭐ Paper Stars ranking: #3256

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! (2023 · 355 citations)
    Current safety alignment methods, such as instruction tuning and RLHF, constrain how a model behaves at inference time.
  2. Visual Adversarial Examples Jailbreak Aligned Large Language Models (2023 · 240 citations)
    Adding image inputs to large language models creates new security risks. The authors argue that images are continuous and high-dimensional, which makes them much easier to manipulate adversarially than discrete text. Because these models can do so many things, a successful attack can do more than cause a…
  3. Safety Alignment Should Be Made More Than Just a Few Tokens Deep (2024 · 97 citations)
    The paper asks why safety-aligned LLMs are so easy to break, whether through adversarial suffixes, prefilling, tweaked decoding parameters, or a few steps of fine-tuning.
  4. Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications (2024 · 66 citations)
    Aligned LLMs can be jailbroken or lose their safeguards even after harmless fine-tuning. The authors ask whether this fragility can be traced to specific parts of the weights. Using the Llama2-chat family, they attribute behaviour to individual neurons and to low-rank components of weight matrices. They use two…
  5. Red-Teaming NSFW Image Classifiers as Text-to-Image Safeguards (2026)
    This paper examines a weakness in NSFW image classifiers that act as output filters for text-to-image (T2I) generators.

All works

15 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. Analyzing Federated Learning through an Adversarial Lens ↗International Conference on Machine Learning · no summary yetArjun Nitin Bhagoji et al.2019 · 388 citations
  2. Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!arXiv:2310.03693Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.2023 · 355 citations
  3. Evolution of social-attribute networks ↗Internet Measurement Conference · no summary yetNeil Zhenqiang Gong et al.2012 · 170 citations
  4. Safety Alignment Should Be Made More Than Just a Few Tokens DeeparXiv:2406.05946Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, et al.2024 · 97 citations
  5. Preserving Link Privacy in Social Network Based Systems ↗arXiv (Cornell University) · no summary yetPrateek Mittal, Charalampos Papamanthou, Dawn Xiaodong Song2012 · 91 citations
  6. Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank ModificationsarXiv:2402.05162Boyi Wei, Kaixuan Huang, Yangsibo Huang, et al.2024 · 66 citations
  7. Certifiably Robust RAG against Retrieval Corruption ↗IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · no summary yetChong Xiang et al.2024 · 39 citations
  8. Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+ ↗arXiv (Cornell University) · no summary yetNeil Zhenqiang Gong et al.2012 · 38 citations
  9. New Directions in Social Authentication ↗no summary yetSakshi Jain et al.2015 · 7 citations
  10. AI Risk Management Should Incorporate Both Safety and Security ↗arXiv (Cornell University) · no summary yetXiangyu Qi et al.2024 · 3 citations
  11. ASPIRE: iterative specification synthesis for security ↗Workshop on Hot Topics in Operating Systems · no summary yetKevin Zhijie Chen et al.2015 · 3 citations
  12. The Model Hears You: Audio Language Model Deployments Should Consider the Principle of Least Privilege ↗Proceedings of the AAAI/ACM Conference on AI Ethics and Society · no summary yetLuxi He et al.2025 · 1 citation
  13. VFIX: Facilitating Software Maintenance of Smart Contracts via Automatically Fixing Vulnerabilities ↗IEEE International Conference on Software Maintenance and Evolution (ICSME) · no summary yetPengcheng Fang et al.2024 · 1 citation

 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 700 times (of the 149,439 papers whose bibliographies we read); 155 citing papers with a page here →.

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