Machine Learning Safety - Edgentiq

Building Trustworthy AI: The FAME Framework for Verifiable Reliability

SafeCoop: Securing Language-Based Communication in Collaborative Autonomous Driving

Unveiling AI’s Inner Workings: A New Dataset for Monitoring and Guiding Safety Behaviors

Enhancing AI Safety: A Training-Free Approach to Detecting Out-of-Distribution Data

The Silent Threat: Poisoning Risks in LLM Prompt Optimization

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Computational Hurdles in Verifying Graph Neural Networks with Global Readout

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The Paradox of Agentic Backdoors: How Multi-Step Triggers Stealthily Control and Enhance AI Agents

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Unmasking the Fragility of AI Agents: A New Framework for Tool Selection Security

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Adaptive Risk Control for Secure and Efficient LLM In-Context Learning

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Advancing Neural Network Reliability with a New Probabilistic Verification Method

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Advancing AI Safety: A Scalable Approach to Probabilistic Robustness for Semantic Segmentation

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Ensuring Trust: A Framework for Certifying Safe and Reliable AI Systems

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Mapping Training Data to Enhance Generative AI Safety

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Enhancing Vision-Language Model Safety with Principled Reasoning

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AI’s New Compass: Using Counterfactuals to Spot Out-of-Distribution Data

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Strategic Deflection: A Smart Defense for AI Against Logit Manipulation Attacks

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The Purple Agent: A Game-Changing Defense Against LLM Jailbreaking

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Gen AI News and Updates

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Building Trustworthy AI: The FAME Framework for Verifiable Reliability

October 28, 2025

SafeCoop: Securing Language-Based Communication in Collaborative Autonomous Driving

October 23, 2025

Unveiling AI’s Inner Workings: A New Dataset for Monitoring and Guiding Safety Behaviors

October 22, 2025