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EDGENT
IQ
News & Current Events
Insights & Perspectives
Analytical Insights & Perspectives
Financial Sector Fortifies Against Surging AI-Powered Scams
Analytical Insights & Perspectives
Deloitte’s 2025 Outlook: Navigating Escalating AI Challenges in Human Capital
Analytical Insights & Perspectives
Salesforce Study Reveals Data Quality is Pivotal for Employee Trust in AI Adoption
Analytical Insights & Perspectives
Top Executives Sidestep Company AI Guidelines, Fueling Shadow AI Risks
Analytical Insights & Perspectives
Intel’s Evolving IP Strategy: A Calculated Shift Towards Core AI Innovation
Analytical Insights & Perspectives
Generative AI Prompts Increased Workforce Surveillance in Indian IT Sector
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Hidden Visual Triggers: Unveiling Backdoor Attacks in AI Embodied Agents
New Research Reveals How LLM Backdoors Can Survive Knowledge Distillation
Securing Vehicular Networks: Understanding and Defending Against Cyber Threats in Distributed Federated Learning
TED++: A New Defense Against Stealthy Backdoor Attacks in Deep Neural Networks
Unmasking Hidden Threats: Dual-Trigger Backdoors in Graph Foundation Models
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New Research Unveils Self-Erasing Backdoor Attacks in AI Models
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Unmasking Hidden Threats: How Targeted Backdoor Attacks Exploit Vision-Language-Action Models
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Understanding Reasoning-based Backdoor Attacks in Large Language Models
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Unmasking Hidden Threats: Backdoor Vectors in Model Merging
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The Paradox of Agentic Backdoors: How Multi-Step Triggers Stealthily Control and Enhance AI Agents
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Exploiting Geometry: Backdoor Attacks on Hyperbolic AI
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Unmasking Deception: How AI Models Learn to Spot Their Own Hidden Backdoors
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Inoculation Prompting: A New Method to Control Unwanted Traits in Language Models
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Poison-to-Poison: A New Strategy to Secure Large Language Models from Backdoor Attacks
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The Stealthy Threat of Deceptive AI Reasoning: Introducing DecepChain
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Securing Federated Learning: A New Defense Against Stealthy Backdoor Attacks
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Advancing Stealth and Efficacy in Clean-Label Backdoor Attacks on Deep Neural Networks
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Hidden Vulnerabilities: How Deep Learning Compilers Can Introduce Backdoors into AI Models
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Securing Large Language Models: A New Fingerprinting Method Using Conversational Context
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Detecting Hidden Threats in Language Models: A New Approach to Backdoor Sample Identification
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The Paradox of Poisoning Advanced LLMs: New Attacks, Unexpected Robustness
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The Hidden Risks of Grouped Queries in AI Conversations
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Advertisement Embedding Attacks: A New Stealthy Threat to LLMs and AI Agents
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Enhancing Stealthy Backdoor Attacks in Text AI Through Strategic Data Selection
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Protecting Federated Learning from Backdoor Attacks with Vision-Language Models
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Unveiling Hidden Dangers in Active Learning: How Selection Functions Can Be Exploited
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FLAT: A New Backdoor Attack with Dynamic, Arbitrary-Target Triggers in Federated Learning
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A New Approach to Securing Language Models from Hidden Backdoors
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Safeguarding AI: A Proactive Approach to Detecting Hidden Backdoor Attacks
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New Research Uncovers Backdoor Vulnerabilities in AI Face Detection Systems
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Assessing Neural Network Resilience with Neuron Coverage Change Rate
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Unmasking VisualTrap: A New Backdoor Threat to AI-Powered GUI Agents
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New Research Uncovers Stealthy Backdoors in Deep Reinforcement Learning Supply Chains
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BackFed: A New Standard for Evaluating Backdoor Attacks in Federated Learning
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Hidden Visual Triggers: Unveiling Backdoor Attacks in AI Embodied Agents
November 3, 2025
New Research Reveals How LLM Backdoors Can Survive Knowledge Distillation
October 22, 2025
Securing Vehicular Networks: Understanding and Defending Against Cyber Threats in Distributed Federated Learning
October 20, 2025
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