All AI Security & AI Safety Posts
-
AI Security
Explainable AI Frameworks in 2026: What Still Ships
This page recommended eight explainable AI frameworks in 2021. Only SHAP is still releasing. A census of what stopped, what replaced it, and where the interpretability effort went instead.
Read More » -
AI Security
Meta-Attacks: Using Machine Learning to Break Machine Learning
Four research groups have published different attacks under the name meta-attack, and they share no threat model. The machine-learning attack on machine learning that ran at scale in 2026 was one instruction, sent a hundred thousand times.
Read More » -
AI Security
Saliency Attacks Are Two Attacks, and the Second One Forges Evidence
Saliency attacks name two unrelated attacks. In one, the attacker uses saliency to find a cheap perturbation. In the other, the explanation is the target, the prediction stays correct, and the forgery defeats the detectors built to catch it.
Read More » -
AI Security
Attacks on Models That Learn While You Watch
Real-time inference and online learning are different architectures with different threat models. Where attacker-influenced data can reach a parameter update, two research groups now find that sequence decides whether poison takes hold, not volume.
Read More » -
AI Security
What Model Inversion Attacks Actually Recover
Model inversion recovers class representatives more often than individuals, its dominant evaluation framework counts adversarial examples as successful reconstructions, and embedding inversion has become a separate privacy problem in enterprise retrieval systems.
Read More » -
AI Security
Data Spoofing Is Four Attacks, and Your Model Answers None of Them
Channel authenticity, measurement integrity and model correctness are three separate properties, and no one of them supplies the other two. Sorting the four attacks that get filed under data spoofing, and the control that answers each.
Read More » -
AI Security
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 targeted disinformation and implementing comprehensive strategies to combat it, society can better protect itself against these sophisticated threats.
Read More » -
AI Security
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 architecture that involves multiple layers of protection. Transport Layer Security (TLS) should be implemented to ensure data confidentiality and integrity during transmission. On the authentication…
Read More »
