All AI Security & AI Safety Posts
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AI Security
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. As machine learning integrates increasingly with sensitive sectors like healthcare, finance, and national security, the imperative for employing encryption techniques that are both potent and…
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AI Security
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 because most algorithms are designed to fit their parameters as closely as possible to the training data. An attacker with sufficient knowledge of the dataset…
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AI Security
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 aim to add noise or make pixel-level changes to deceive machine learning models, semantic attacks target the inherent understanding of the data. For example, instead…
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AI Security
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 processes with those of humans, no matter how sophisticated or powerful the AI system becomes. Our trust in the future of AI rests on whether…
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AI Security
A (Very) Brief History of AI
As early as the mid-19th century, Charles Babbage and Ada Lovelace created the Analytical Engine, a mechanical general-purpose computer. Lovelace is often credited with the idea of a machine that could manipulate symbols in accordance with rules and that it might act upon other than just numbers, touching upon concepts central to AI.
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AI Security
Understanding and Addressing Biases in Machine Learning
While ML offers extensive benefits, it also presents significant challenges, among them, one of the most prominent ones is biases in ML models. Bias in ML refers to systematic errors or influences in a model's predictions that lead to unequal treatment of different groups. These biases are problematic as they can reinforce existing inequalities and unfair practices, translating to real-world consequences like discriminatory hiring or…
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AI Security
Adversarial Attacks: The Hidden Risk in AI Security
Adversarial attacks specifically target the vulnerabilities in AI and ML systems. At a high level, these attacks involve inputting carefully crafted data into an AI system to trick it into making an incorrect decision or classification. For instance, an adversarial attack could manipulate the pixels in a digital image so subtly that a human eye wouldn't notice the change, but a machine learning model would…
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AI Security
Gradient-Based Attacks: A Dive into Optimization Exploits
Gradient-based attacks refer to a suite of methods employed by adversaries to exploit the vulnerabilities inherent in ML models, focusing particularly on the optimization processes these models utilize to learn and make predictions. These attacks are called “gradient-based” because they primarily exploit the gradients, mathematical entities representing the rate of change of the model’s output with respect to its parameters, computed during the training of…
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