Not All LLM Reasoners Are Created Equal

The jury is still out on whether current LLMs can truly reason. They appear to reason, but then fail in surprising ways. And failure modes are a hacker's best friend.

This research caught my eye:

Just because models have high scores on GSM8K [Grade School Math benchmark] doesn't mean they can solve two linked questions!

Our work uncovers a significant reasoning gap in LLMs, especially in smaller, cost-efficient, and math-specialized models

How did we test LLMs?

By evaluating them on 'compositional GSM' where the answer to Question-1 is a key variable in Question-2! They must ace Q1 to solve Q2

The TLDR: models get distracted: even models that ace math benchmarks (Hendrycks Math) struggle to answer question 2 correctly.

The researchers concluded that reasoning is both contextual and compositional. Yet current evaluation models don't often consider this latter element (!).

Why is this relevant to security? It highlights a potential vulnerability in AI systems used for decision-making in security contexts. If an AI can't consistently reason through linked problems, it might miss crucial connections in threat analysis or incident response scenarios. This "reasoning gap" could be exploited by adversaries to mislead AI-powered security tools.

For security professionals, this underscores the importance of not blindly trusting AI outputs, especially in complex, multi-step reasoning tasks. Always verify AI-generated conclusions and maintain human oversight in critical security processes.

What's your experience with AI reasoning in security applications? Have you noticed any surprising failures or limitations?

Cheers, Craig