Friday Squid Blogging: Regulating Squid Fishing in the South Pacific

2026-05-22 · Source: Schneier on Security · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

A recent blog post, initially about South Pacific squid fishing regulation, served as a discussion forum for readers to address diverse security and technology issues. Key discussions highlighted the inherent flaws in AI-based age verification systems, emphasizing the critical need to train AI with adversarial data to counter "dishonest" users and the fundamental unreliability of biometrics. Concerns were also raised about the "enshittification" of the internet by AI-generated "slop" content, particularly voice clones of figures like Richard Feynman, which exploit reputations for ad revenue. Other topics included the targeting of U.S. military personnel using commercial location data, the instability of cosmological models, and a CIFSwitch vulnerability in Linux allowing local root access. The collective commentary underscored persistent challenges in securing digital systems and verifying identities against sophisticated evasion tactics.

Key takeaway

For AI Security Engineers developing identity verification systems, recognize that current biometric and AI-based age checks are fundamentally flawed due to the "gap twixt physical object and sensor." Prioritize adversarial training data and robust fraud detection over relying on static biometric analysis. Consider the legal and ethical implications of systems that inherently discriminate or fail honest users, and advocate for accountability for platforms failing to implement functioning verification.

Key insights

The "gap twixt physical object and sensor" makes digital identity verification inherently vulnerable to circumvention.

Principles

• AI training data must include adversarial examples to be robust.
• Biometric systems are inherently unreliable for high-stakes security.
• Unstable solutions in physics are generally considered not physical.

Method

DNN-based systems can identify software vulnerabilities by generating control-flow graphs and decision trees for code analysis, akin to McCabe Analysis.

In practice

• Report AI-generated "slop" content on platforms like YouTube.
• Question AI systems trained solely on "honest" user data.
• Recognize inherent limits of biometric identification systems.

Topics

• AI Security
• Age Verification
• Biometric Systems
• AI Content Generation
• Data Privacy
• Cybersecurity Vulnerabilities
• National Security

Best for: CTO, VP of Engineering/Data, Computer Vision Engineer, AI Security Engineer, AI Ethicist, Tech Journalist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Schneier on Security.