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Found 15 papers, 4 papers with code
MAD-MAX: Modular And Diverse Malicious Attack MiXtures for Automated LLM Red Teaming
The MAD-MAX approach is designed to be easily extensible with newly discovered attack strategies and outperforms the prominent Red Teaming method Tree of Attacks with Pruning (TAP) significantly in terms of Attack Success Rate (ASR) and queries needed to achieve jailbreaks.
Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs
We find that there is significant performance variation depending on the style of jailbreak a defence is subject to.
Granite Guardian
We introduce the Granite Guardian models, a suite of safeguards designed to provide risk detection for prompts and responses, enabling safe and responsible use in combination with any large language model (LLM).
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation
Despite the impressive adaptability of large language models (LLMs), challenges remain in ensuring their security, transparency, and interpretability.
Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs
Large language models are prone to misuse and vulnerable to security threats, raising significant safety and security concerns.
Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs
This paper presents an approach to developing assurance cases for adversarial robustness and regulatory compliance in large language models (LLMs).
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs
The EU AI Act (EUAIA) introduces requirements for AI systems which intersect with the processes required to establish adversarial robustness.
MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks
The proliferation of Large Language Models (LLMs) in diverse applications underscores the pressing need for robust security measures to thwart potential jailbreak attacks.
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection
The effective detection of evidence of financial anomalies requires collaboration among multiple entities who own a diverse set of data, such as a payment network system (PNS) and its partner banks.
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data.
Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems.
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
Scalable Deployment of AI Time-series Models for IoT
A knowledge-based approach to managing model and time-series data allows the use of general semantic concepts for expressing feature engineering tasks.
Castor: Contextual IoT Time Series Data and Model Management at Scale
The main features of Castor are: (1) an efficient pipeline for ingesting IoT time series data in real time; (2) a scalable, hybrid data management service for both time series and contextual data; (3) a versatile semantic model for contextual information which can be easily adopted to different application domains; (4) an abstract framework for developing and storing predictive models in R or Python; (5) deployment services which automatically train and/or score predictive models upon user-defined conditions.
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