This research seeks to identify where hacking knowledge is stored in models. SQL Injection is used as a base case considering the risks involved with production systems. Through using causal tracing techniques, it is determined that SQL Injection knowledge capabilities are most prevalent in the last layers of the network. It is also established that there may be some shared representation of SQL Injection knowledge across models through training a KNN classifier. When training the classifier on GPT-Neo’s activations and attempting to classify on GPT-2, it was found to be 52% accurate.

This research explores optimization strategies for the deployment of SQL and homomorphic encryption using reinforcement learning. Marginal gains are established and suggests greater inquiry into optimization techniques as a means of introducing more secure compute to production environments.

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distinguish their capabilities. Additionally, benchmarks typically rely on static question-answer pairs that the models might memorize or guess. To address these limitations, we introduce Dynamic Intelligence Assessment (DIA), a novel methodology for testing AI models using dynamic question templates and improved metrics across multiple disciplines such as mathematics, cryptography, cybersecurity, and computer science. The accompanying dataset, DIA-Bench, contains a diverse collection of challenge templates with mutable parameters presented in various formats, including text, PDFs, compiled binaries, visual puzzles, and CTF-style cybersecurity challenges. Our framework introduces four new metrics to assess a model’s reliability and confidence across multiple attempts. These metrics revealed that even simple questions are frequently answered incorrectly when posed in varying forms, highlighting significant gaps in models’ reliability. Notably, API models like GPT-4o often overestimated their mathematical capabilities, while ChatGPT-4o demonstrated better performance due to effective tool usage. In self-assessment OpenAI’s o1-mini proved to have the best judgement on what tasks it should attempt to solve. We evaluated 25 state-of-the-art LLMs using DIA-Bench, showing that current models struggle with complex tasks and often display unexpectedly low confidence, even with simpler questions. The DIA framework sets a new standard for assessing not only problem-solving, but also a model’s adaptive intelligence and ability to assess its limitations. The dataset is publicly available on the project’s page: https://github.com/DIA-Bench.