AI Security
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“Magical” Emergent Behaviours in AI: A Security Perspective
Emergent behaviours in AI have left both researchers and practitioners scratching their heads. These are the unexpected quirks and functionalities…
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How Dynamic Data Masking Reinforces Machine Learning Security
Data masking, also known as data obfuscation or data anonymization, serves as a crucial technique for ensuring data confidentiality and…
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How Label-Flipping Attacks Mislead AI Systems
Label-flipping attacks refer to a class of adversarial attacks that specifically target the labeled data used to train supervised machine…
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Backdoor Attacks in Machine Learning Models
Backdoor attacks in the context of Machine Learning (ML) refer to the deliberate manipulation of a model's training data or…
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Perturbation Attacks in Text Classification Models
Text Classification Models are critical in a number of cybersecurity controls, particularly in mitigating risks associated with phishing emails and…
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How Multimodal Attacks Exploit Models Trained on Multiple Data Types
In simplest terms, a multimodal model is a type of machine learning algorithm designed to process more than one type…
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The Threat of Query Attacks on Machine Learning Models
Query attacks are a type of cybersecurity attack specifically targeting machine learning models. In essence, attackers issue a series of…
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Securing Data Labeling Through Differential Privacy
Differential Privacy is a privacy paradigm that aims to reconcile the conflicting needs of data utility and individual privacy. Rooted…
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Explainable AI Frameworks
Trust comes through understanding. As AI models grow in complexity, they often resemble a "black box," where their decision-making processes…
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Meta-Attacks: Utilizing Machine Learning to Compromise Machine Learning Systems
Meta-attacks represent a sophisticated form of cybersecurity threat, utilizing machine learning algorithms to target and compromise other machine learning systems.…
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How Saliency Attacks Quietly Trick Your AI Models
"Saliency" refers to the extent to which specific features or dimensions in the input data contribute to the final decision…
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Batch Exploration Attacks on Streamed Data Models
Batch exploration attacks are a class of cyber attacks where adversaries systematically query or probe streamed machine learning models to…
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How Model Inversion Attacks Compromise AI Systems
A model inversion attack aims to reverse-engineer a target machine learning model to infer sensitive information about its training data.…
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When AI Trusts False Data: Exploring Data Spoofing’s Impact on Security
Data spoofing is the intentional manipulation, fabrication, or misrepresentation of data with the aim of deceiving systems into making incorrect…
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Twitter API for Secure Data Collection in Machine Learning Workflows
While APIs serve as secure data conduits, they are not impervious to cyber threats. Vulnerabilities can range from unauthorized data…
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