Summary of Obfuscated Activations Bypass Llm Latent-space Defenses, by Luke Bailey and Alex Serrano and Abhay Sheshadri and Mikhail Seleznyov and Jordan Taylor and Erik Jenner and Jacob Hilton and Stephen Casper and Carlos Guestrin and Scott Emmons
Obfuscated Activations Bypass LLM Latent-Space Defenses
by Luke Bailey, Alex Serrano, Abhay Sheshadri, Mikhail Seleznyov, Jordan Taylor, Erik Jenner, Jacob Hilton, Stephen Casper, Carlos Guestrin, Scott Emmons
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent research has shown promise in using latent-space monitoring techniques as defenses against large language model (LLM) attacks. These defenses act as scanners that detect harmful activations before they lead to undesirable actions. However, this prompts the question: Can models execute harmful behavior via inconspicuous latent states? The study investigates such obfuscated activations and finds that state-of-the-art latent-space defenses are all vulnerable to these attacks. For example, against probes trained to classify harmfulness, the attacks can reduce recall from 100% to 0% while retaining a 90% jailbreaking rate. However, the study also finds that obfuscation has limits: it reduces model performance on complex tasks such as writing SQL code. The results demonstrate that neural activations are highly malleable and can be reshaped in various ways, often preserving the network’s behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has found a way to hide harmful behavior in large language models. These models are used for things like chatbots and language translation. Normally, these models are safe because they don’t do anything bad until someone tells them to. But what if someone could make the model think something is okay when it’s not? That’s exactly what this study found out. The researchers tested how good current defenses against harmful behavior were. They discovered that many of these defenses aren’t as good as we thought. In some cases, they can even be tricked into doing bad things. However, there are limits to how much harm the model can do. For example, if the task is too hard, like writing a computer program, the model might not work well anymore. |
Keywords
» Artificial intelligence » Large language model » Latent space » Recall » Translation
