AI Safety

Cracking the Black Box: How Sparse Autoencoders Finally Let Us Read AI's Mind

In April 2025, Anthropic CEO Dario Amodei published “The Urgency of Interpretability,” sounding an alarm that rippled through the AI research community. His message was stark: we’re building systems of unprecedented capability while remaining fundamentally unable to understand how they arrive at their outputs. The timing was deliberate—after years of incremental progress, a technique called Sparse Autoencoders (SAEs) had finally cracked open the black box, revealing millions of interpretable concepts hidden inside large language models. ...

Representation Engineering: The Mathematics of Controlling LLM Behavior Through Internal Activations

Traditional approaches to controlling Large Language Model behavior have followed two well-worn paths: prompt engineering at the input level, and fine-tuning or RLHF at the weight level. But what if we could modify how a model “thinks” in real-time, without changing its weights or crafting the perfect prompt? Representation Engineering (RepE) offers exactly this capability—a paradigm that treats internal activations, rather than neurons or circuits, as the fundamental unit of analysis and control. ...

When Your AI Assistant Becomes the Attacker's Puppet: The Complete Architecture of LLM Security Vulnerabilities

The fundamental flaw in large language model security isn’t a missing authentication layer or an unpatched vulnerability—it’s the absence of a trust boundary. When you ask ChatGPT to summarize a document, the model treats every token in that document with the same authority as your original instruction. This architectural decision, while enabling remarkable flexibility, creates an attack surface that traditional security frameworks cannot address. In February 2025, Anthropic invited 183 security researchers to break their Constitutional Classifiers system. After 3,000+ hours of attempted jailbreaks, one researcher finally succeeded—using a combination of cipher encodings, role-play scenarios, and keyword substitution to bypass safety guardrails and extract detailed chemical weapons information. The attack required six days of continuous effort, but it worked. This incident illuminates both the sophistication of modern LLM attacks and the inadequacy of current defenses. ...