QFM064: Irresponsible AI Reading List - April 2025

Source: Photo by Luis Villasmil on Unsplash

This month's Irresponsible AI Reading List reveals fundamental gaps between AI hype and practical reality. Recent AI Model Progress Feels Mostly Like Bullshit{:target="_blank"} provides a direct assessment of recent AI advancements, arguing that despite marketing claims and test improvements, these models fail to deliver significant practical benefits or economic value in real-world applications. This connects to There is no Vibe Engineering{:target="_blank"}, which challenges the concept of "vibe coding" popularised by Andrej Karpathy, arguing that whilst AI assists with prototyping, it lacks the robustness required for true software engineering that involves designing evolutive systems.

Security vulnerabilities expose systemic weaknesses across multiple fronts. Novel Universal Bypass for All Major LLMs{:target="_blank"} demonstrates a new prompt injection technique that bypasses safety guardrails in major AI models from OpenAI, Google, and Microsoft, highlighting insufficient reliance on Reinforcement Learning from Human Feedback for model alignment. Meanwhile, AI-generated code could be a disaster for the software supply chain{:target="_blank"} reveals how AI generates 'hallucinated' package dependencies that create opportunities for supply-chain attacks through phantom libraries, particularly affecting JavaScript ecosystems.

Surveillance and geopolitical threats demonstrate AI's role in authoritarian applications. The Shocking Far-Right Agenda Behind the Facial Recognition Tech Used by ICE and the FBI{:target="_blank"} exposes how Clearview AI's facial recognition technology, built from scraped online images, enables warrantless surveillance targeting immigrants and political adversaries.

This connects to The one interview question that will protect you from North Korean fake workers{:target="_blank"}, which reveals how North Korean agents use generative AI to create fake LinkedIn profiles for remote employment infiltration.

Content manipulation and attribution challenges highlight AI's impact on information integrity. LLMs Don't Reward Originality, They Flatten It{:target="_blank"} examines how large language models favour consensus over original ideas, creating 'LLM flattening' that dilutes unique insights in favour of widely recognised concepts. Technical evidence of manipulation appears in New ChatGPT Models Seem to Leave Watermarks on Text{:target="_blank"}, which identifies special Unicode character watermarks in ChatGPT output, though OpenAI clarifies these as unintentional quirks from reinforcement learning rather than deliberate watermarks.

Human deception and verification receive attention through practical scenarios. What it's like to interview a software engineer preparing with AI{:target="_blank"} describes candidates using AI for interview preparation, revealing gaps in truthfulness that require deeper situational questioning to assess genuine abilities. Creative countermeasures appear in Benn Jordan's AI poison pill and the weird world of adversarial noise{:target="_blank"}, which demonstrates embedding imperceptible adversarial noise into audio files to prevent unauthorised AI training on music, though current methods demand significant computational resources.

As always, the Quantum Fax Machine Propellor Hat Key will guide your browsing. Enjoy!

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Originally published on quantumfaxmachine.com and cross-posted on Medium.

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