AI Security
PostQuantum.com – Industry news and blog on Quantum Computing, Quantum Security, PQC, Post-Quantum, AI Security, AI, ML
-
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 learning models. In a typical label-flipping attack, the attacker changes the labels associated with the training data points, essentially turning…
Read More » -
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 its algorithmic logic to implant a hidden vulnerability, often referred to as a "trigger." Unlike typical vulnerabilities that are discovered…
Read More » -
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 spam. However, the emergence of sophisticated perturbation attacks poses substantial threats, manipulating models into erroneous classifications and exposing inherent vulnerabilities.…
Read More » -
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 of data, be it text, images, audio, or even video. Traditional models often specialize in one form of data; for…
Read More » -
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 queries, usually input data fed into the model, to gain insights from the model's output. This could range from understanding…
Read More » -
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 in the mathematical theories of privacy and cryptography, Differential Privacy offers quantifiable privacy guarantees and has garnered substantial attention for…
Read More » -
Explainable AI Frameworks
Trust comes through understanding. As AI models grow in complexity, they often resemble a "black box," where their decision-making processes become increasingly opaque. This lack of transparency can be a roadblock, especially when we need to trust and understand these…
Read More » -
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. Unlike traditional cyberattacks, which may employ brute-force methods or exploit software vulnerabilities, meta-attacks are more nuanced, leveraging the intrinsic weaknesses…
Read More » -
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 made by the model. Mathematically, this is often quantified by analyzing the gradients of the model's loss function with respect…
Read More » -
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 expose vulnerabilities, glean sensitive information, or decipher the underlying structure and parameters of the models. The motivation behind such attacks…
Read More » -
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. Specifically, these attacks are designed to exploit the model's internal representations and decision boundaries to reverse-engineer and subsequently reveal sensitive…
Read More » -
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 decisions or assessments. While it is often associated with IP address spoofing in network security, the concept extends into various…
Read More » -
Targeted Disinformation
Targeted disinformation poses a significant threat to societal trust, democratic processes, and individual well-being. The use of AI in these disinformation campaigns enhances their precision, persuasiveness, and impact, making them more dangerous than ever before. By understanding the mechanisms of…
Read More » -
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 access and leakage to more severe threats like remote code execution attacks. Therefore, it's crucial to integrate a robust security…
Read More » -
The Dark Art of Model Stealing: What You Need to Know
Model stealing, also known as model extraction, is the practice of reverse engineering a machine learning model owned by a third party without explicit authorization. Attackers don't need direct access to the model's parameters or training data to accomplish this.…
Read More »
