AI Security
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Why AI Cannot Break Modern Encryption
AI cannot break modern encryption. The reasons are fundamental: Mathematical Hardness, Cryptographic Design, Empirical Track Record, Quantum Contrast, Expert Consensus.
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Closing the Gap Between AI Principles and AI Reality
In pursuit of AI systems that are ethical and robust we are seeing the emergence of an, ironically, ethical challenge: firms rushing to position themselves as leaders in Responsible AI without the necessary depth in technical expertise. Though their motivations…
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Post-Quantum Cryptography (PQC) Meets Quantum AI (QAI)
Post-Quantum Cryptography (PQC) and Quantum Artificial Intelligence (QAI) are converging fields at the forefront of cybersecurity. PQC aims to develop cryptographic algorithms that can withstand attacks by quantum computers, while QAI explores the use of quantum computing and AI to…
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Full Stack of AI Concerns: Responsible, Safe, Secure AI
As AI continues to evolve and integrate deeper into societal frameworks, the strategies for its governance, alignment, and security must also advance, ensuring that AI enhances human capabilities without undermining human values. This requires a vigilant, adaptive approach that is…
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The Dual Risks of AI Autonomous Robots: Uncontrollable AI Meets Cyber-Kinetic Risks
The automotive industry has revolutionized manufacturing twice. The first time was in 1913 when Henry Ford introduced a moving assembly line at his Highland Park plant in Michigan. The innovation changed the production process forever, dramatically increasing efficiency, reducing the…
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Marin’s Statement on AI Risks
The rapid development of AI brings both extraordinary potential and unprecedented risks. AI systems are increasingly demonstrating emergent behaviors, and in some cases, are even capable of self-improvement. This advancement, while remarkable, raises critical questions about our ability to control…
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AI Security 101
Artificial Intelligence (AI) is no longer just a buzzword; it’s an integral part of our daily lives, powering everything from our search for a perfect meme to critical infrastructure. But as Spider-Man’s Uncle Ben wisely said, “With great power comes…
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Why We Seriously Need a Chief AI Security Officer (CAISO)
With AI’s breakneck expansion, the distinctions between ‘cybersecurity’ and ‘AI security’ are becoming increasingly pronounced. While both disciplines aim to safeguard digital assets, their focus and the challenges they address diverge in significant ways. Traditional cybersecurity is primarily about defending…
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How to Defend Neural Networks from Trojan Attacks
Neural networks learn from data. They are trained on large datasets to recognize patterns or make decisions. A Trojan attack in a neural network typically involves injecting malicious data into this training dataset. This 'poisoned' data is crafted in such…
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Model Fragmentation and What it Means for Security
Model fragmentation is the phenomenon where a single machine-learning model is not used uniformly across all instances, platforms, or applications. Instead, different versions, configurations, or subsets of the model are deployed based on specific needs, constraints, or local optimizations. This…
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Outsmarting AI with Model Evasion
Model Evasion in the context of machine learning for cybersecurity refers to the tactical manipulation of input data, algorithmic processes, or outputs to mislead or subvert the intended operations of a machine learning model. In mathematical terms, evasion can be…
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Securing Machine Learning Workflows through Homomorphic Encryption
Homomorphic Encryption has transitioned from being a mathematical curiosity to a linchpin in fortifying machine learning workflows against data vulnerabilities. Its complex nature notwithstanding, the unparalleled privacy and security benefits it offers are compelling enough to warrant its growing ubiquity.…
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Understanding Data Poisoning: How It Compromises Machine Learning Models
Data poisoning is a targeted form of attack wherein an adversary deliberately manipulates the training data to compromise the efficacy of machine learning models. The training phase of a machine learning model is particularly vulnerable to this type of attack…
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Semantic Adversarial Attacks: When Meaning Gets Twisted
Semantic adversarial attacks represent a specialized form of adversarial manipulation where the attacker focuses not on random or arbitrary alterations to the data but specifically on twisting the semantic meaning or context behind it. Unlike traditional adversarial attacks that often…
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The AI Alignment Problem
The AI alignment problem sits at the core of all future predictions of AI’s safety. It describes the complex challenge of ensuring AI systems act in ways that are beneficial and not harmful to humans, aligning AI goals and decision-making…
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