How MIT students are helping to prevent cyberattacks
Students from the MIT Cybersecurity Clinic help local governments and other vulnerable organizations defend against digital threats.
More from the blog
From MIT to IBM, expediting AI and quantum deployment
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
System helps humans predict when self-driving cars will make mistakes
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
Walter Torous named executive director of MIT Center for Real Estate
The senior lecturer, already director of the degree program, will now oversee all aspects of the center’s activities and operations.
Ila Kumar: Innovating with communities
The PhD student works to give young people an active role in shaping digital technologies that can support their own well-being.
Related papers
`From Prompt to Perturbation': An Adaptive Framework for Voice-Based Jailbreaks on Audio LLMs
An adaptive jailbreak attack framework for systematic evaluation of both cascaded pipelines and end-to-end large audio-language models under a unified experimental setting that achieves consistently higher attack success rates across diverse audio-based LLM systems.
SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces
This work presents SkillSafetyBench, a runnable benchmark for evaluating skill-facing safety failures, and suggests that agent safety depends not only on model-level alignment, but also on how agents interpret skills, trust workflow context, and act through executable environments.
Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
Retrieval-Augmented Generation (RAG) systems are emerging as a key approach for grounding Large Language Models (LLMs) in external knowledge, addressing limitations in factual accuracy and contextual relevance. However, there is a lack of empirical studies that report on the development of RAG-based implementations grounded in real-world use cases, evaluated through general user involvement, and accompanied by systematic documentation of lessons learned. This paper presents five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics. Each system incorporates multilingual OCR, semantic retrieval via vector embeddings, and domain-adapted LLMs, deployed through local servers or cloud APIs to meet distinct user needs. A web-based evaluation involving a total of 100 participants assessed the systems across six dimensions: (i) Ease of Use, (ii) Relevance, (iii) Transparency, (iv) Responsiveness, (v) Accuracy, and (vi) Likelihood of Recommendation. Based on user feedback and our development experience, we documented twelve key lessons learned, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.
Jailbreaking in the Haystack
NINJA (short for Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LMs by appending benign, model-generated content to harmful user goals to reveal fundamental vulnerabilities in modern LMs.