Interactive Security Training’s Post

National Institute of Standards and Technology (NIST) just released a comprehensive taxonomy and terminology of attacks and mitigations for machine learning in adversarial settings. Very useful if you work in this space. Full report 👇 https://lnkd.in/exKbAfCd

Exciting News! I am thrilled to share that my recent publication, titled "FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks" has been officially published in ACSAC '23! 🔗 Check it out here: https://lnkd.in/gQZxJuCD

 New Privacy-Preserving Federated Learning Blog Post!: New Privacy-Preserving Federated Learning Blog Post! Dear Colleagues,    ln our last Privacy-Preserving Federated Learning (PPFL) post, we explored the problem of providing input privacy in PPFL systems for the horizontally-partitioned setting. In this new post, Protecting Model Updates in Privacy-Preserving Federated Learning: Part Two, we focus on techniques for providing input privacy when data is vertically … Continue reading  New Privacy-Preserving Federated Learning Blog Post!

I am curious to learn about: 1. Your thoughts on the sensitivity of information contained in LLM system prompts? 2. Is the 'leakage' of prompts behind LLM agents/applications interesting to the LLM security community? Or would companies and AI applications release system prompts for transparency and trust? In our recent work on AI Security, we investigate the prompt leakage effect in LLMs (https://lnkd.in/gtp6V-MD), focusing on multi-turn interactions and studying the leakage of specific prompt contents. We perform an in-depth study to measure the mitigation effects of applying different defense strategies, to both open- and closed- source LLMs. Paper - https://lnkd.in/g5Sc5dYz Work done at Salesforce Research in collaboration w/ Alexander Fabbri, Benjamin R. Philippe Laban, Shafiq Joty and Chien-Sheng (Jason) WU.

Here are three valuable datasets useful for making LLM's behave. Used with a process called "red teaming" (think of toxic trolls trying to make the worst out of your LLM) they can help your LLM's avoid unethical behavour. 𝐀𝐭𝐭𝐚𝐐: (ment to provoke LLM's to commit crimes and acts of deception) https://lnkd.in/dZeCRkM5 𝐒𝐨𝐜𝐢𝐚𝐥𝐒𝐭𝐢𝐠𝐦𝐚: (ment to draw out social bias & racist responses) https://lnkd.in/dahGRGZK 𝐀𝐮𝐫𝐨𝐫𝐚: (prevent misuse in cyberattacks and the discovery of software vulnerabilities) https://lnkd.in/dZeCRkM5 Also kudos to huggingface for assembling and sharing so many valuable assets for ML and Deeplearning.

Adversarial Machine Learning refers to the study and development of techniques where an adversary manipulates or exploits vulnerabilities in machine learning models. The goal is to deceive the model's predictions or classifications by introducing carefully crafted input data, known as adversarial examples. These examples are designed to be perceptually similar to regular data but can lead the model to make incorrect predictions. #nist #niststandards National Institute of Standards and Technology (NIST)

Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations https://lnkd.in/eBea6Zcc

5 Ways Artificial Intelligence Is (Quietly) Changing Libraries https://lnkd.in/df2AaAnh

Exciting news! Our project, "Deep Fake Detection and Model Evaluation," is making significant strides in countering the threats posed by deepfake technology. Collaborating with my peers Jainav Mutha, and Deep Mehta, and mentors Kelly Wu and Saniat Sohrawardi, we're committed to enhancing deepfake detection methods to safeguard against misinformation and privacy breaches. 🚀 𝑮𝒐𝒂𝒍 𝒂𝒏𝒅 𝑺𝒄𝒊𝒆𝒏𝒕𝒊𝒇𝒊𝒄 𝑴𝒆𝒓𝒊𝒕: 🎯 Our project aims to re-implement benchmarking detection models using PyTorch, validate these models on challenging datasets such as WildDeepFakes, and implement uncertainty estimation techniques to enhance model robustness. We plan to address the urgent need for reliable deepfake detection to mitigate the risks of technological deception. By rigorously evaluating benchmark models and leveraging interdisciplinary methodologies, we're hoping to contribute to the advancement of detection methods crucial in today's digital landscape. 𝑨𝒑𝒑𝒓𝒐𝒂𝒄𝒉: 🔬 Model Selection: Utilizing the UCF Spatial model for its competitive performance and spatial analysis expertise. Real-World Validation: Validating on challenging datasets like WildDeepFakes to address real-world scenarios. Empirical Decision-Making: Guiding our approach with empirical findings to ensure informed decisions. 𝑰𝒏𝒕𝒆𝒓𝒎𝒆𝒅𝒊𝒂𝒕𝒆 𝑹𝒆𝒔𝒖𝒍𝒕𝒔: 📊 We evaluated the UCF model on the FaceForensics++ dataset using the UCF Spatial model, achieving impressive results. Accuracy: 88.12% AUC: 93.87% Precision: 97.23% These results underscore the robustness and reliability of our approach to detecting deepfake content. 𝑵𝒆𝒙𝒕 𝒔𝒕𝒆𝒑𝒔: 🧗♂️ In our upcoming work, we plan to validate our models on challenging datasets, such as WildDeepFakes, to assess their performance under more diverse and realistic conditions. Additionally, we aim to implement uncertainty estimation techniques to enhance the robustness of our models to improve the model's ability to recognize and flag uncertain or ambiguous instances, thereby increasing its reliability in real-world scenarios. 𝑭𝒖𝒕𝒖𝒓𝒆 𝑶𝒖𝒕𝒍𝒐𝒐𝒌: 🔎 As we continue our research, our goal is to set new standards in accuracy, reliability, and transparency in deepfake detection. By sharing our codebase and datasets, we aim to foster collaboration and drive innovation in the field. #DeepfakeDetection #ModelEvaluation #DataScience #DeepLearning #PyTorch #Research #Collaboration #DigitalSecurity #TechnologyEthics

Excited to share our latest blog post on "Mitigating Cascading Effects in Large Adversarial Graph Environments." The post digs into the critical issue of minimizing the potential harm caused by cascading impacts in various infrastructure networks. It emphasizes the importance of preemptively prioritizing defense targets and leveraging deep learning to identify strategies to counter potential threats. Check out the detailed insights and findings at https://bit.ly/3Waye7G. #GraphEnvironments #CascadingEffects #DeepLearning #AdversarialDefense