Vulnerabilities in Large Language Models

In this episode of Crazy Wisdom, Stewart Alsop welcomes Christopher Canal, co-founder of Equistamp, for a deep discussion on the current state of AI evaluations (evals), the rise of agents, and the safety challenges surrounding large language models (LLMs). Christopher breaks down how LLMs function, the significance of scaffolding for AI agents, and the complexities of running evals without data leakage. The conversation covers the risks associated with AI agents being used for malicious purposes, the performance limitations of long time horizon tasks, and the murky realm of interpretability in neural networks. Additionally, Christopher shares how Equistamp aims to offer third-party evaluations to combat principal-agent dilemmas in the industry. For more about Equistamp's work, visit Equistamp.com to explore their evaluation tools and consulting services tailored for AI and safety innovation.

Check out this GPT we trained on the conversation!

Timestamps

00:00 Introduction and Guest Welcome

00:13 The Importance of Evals in AI

01:32 Understanding AI Agents

04:02 Challenges and Risks of AI Agents

07:56 Future of AI Models and Competence

16:39 The Concept of Consciousness in AI

19:33 Current State of Evals and Data Leakage

24:30 Defining Competence in AI

31:26 Equistamp and AI Safety

42:12 Conclusion and Contact Information

Key Insights

1. The Importance of Evals in AI Development: Christopher Canal emphasizes that evaluations (evals) are crucial for measuring AI models' capabilities and potential risks. He highlights the uncertainty surrounding AI's trajectory and the need to accurately assess when AI systems outperform humans at specific tasks to guide responsible adoption. Without robust evals, companies risk overestimating AI's competence due to data leakage and flawed benchmarks.
2. The Role of Scaffolding in AI Agents: The conversation distinguishes between large language models (LLMs) and agents, with Christopher defining agents as systems operating within a feedback loop to interact with the world in real time. Scaffolding—frameworks that guide how an AI interprets and responds to information—plays a critical role in transforming static models into agents that can autonomously perform complex tasks. He underscores how effective scaffolding can future-proof systems by enabling quick adaptation to new, more capable models.
3. The Long Tail Challenge in AI Competence: AI agents often struggle with tasks that have long time horizons, involving many steps and branching decisions, such as debugging or optimizing machine learning models. Christopher points out that models tend to break down or lose coherence during extended processes, a limitation that current research aims to address with upcoming iterations like GPT-4.5 and beyond. He speculates that incorporating real-world physics and embodied experiences into training data could improve long-term task performance.
4. Ethical Concerns with AI Applications: Equistamp takes a firm stance on avoiding projects that conflict with its core values, such as developing AI models for exploitative applications like parasocial relationship services or scams. Christopher shares concerns about how easily AI agents could be weaponized for fraudulent activities, highlighting the need for regulations and more transparent oversight to mitigate misuse.
5. Data Privacy and Security Risks in LLMs: The episode sheds light on the vulnerabilities of large language models, including shared cache issues that could leak sensitive information between different users. Christopher references a recent paper that exposed how timing attacks can identify whether a response was generated by hitting the cache or computing from scratch, demonstrating potential security flaws in API-based models that could compromise user data.
6. The Principal-Agent Dilemma in AI Evaluation: Stewart and Christopher discuss the conflict of interest inherent in companies conducting their own evals to showcase their models' performance. Christopher explains that third-party evaluations are essential for unbiased assessments. Without external audits, organizations may inflate claims about their models' capabilities, reinforcing the need for independent oversight in the AI industry.
7. Equistamp’s Mission and Approach: Equistamp aims to fill a critical gap in the AI ecosystem by providing independent, safety-oriented evaluations and consulting services. Christopher outlines their approach of creating customized evaluation frameworks that compare AI performance against human baselines, helping clients make informed decisions about deploying AI systems. By prioritizing transparency and safety, Equistamp hopes to set a new standard for accountability in the rapidly evolving AI landscape.