Dodging Latent Space Detectors: Obfuscated Activation Attacks with Luke, Erik, and Scott.
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What are latent‑space defenses and why are they needed?
If the model is doing some highly capable, sophisticated behavior, like if it is implanting a a a quite sophisticated backdoor in your code. This doesn't happen by accident. The hope is That the language model activations already have this lower dimensional or this higher level representation of the sophisticated
behavior that we're seeing. Someone might poison your training data. So like all these models are trained by just collecting data from the open internet. So someone can just upload something to a website and then eventually it's going to be scraped and end up in the training data. So the behavior that the attacker is trying to elicit from the model may actually be high loss to begin with. The more of reasoning moves away from natural language into model latence, I think the harder it gets to monitor models. And I do think that's sort of one of the scariest things going on right now from a safety perspective.
Hello and welcome back to the Cognitive Revolution. Today I'm speaking with Luke Bailey, Eric Jenner, and Scott Emmons, who, along with their fellow lead authors, Alex Sarano, Abe Shashadri, Mikhail Seletioff, and Jordan Taylor, have recently published a fascinating new paper called Obfuscated Activations Bypass Large Language Model Latent Based Defenses. Latent based defenses, regular listeners will know, include a number of techniques for classifying and in some cases interpreting large language models' internal activation patterns with the hope of detecting and preventing harmful or otherwise undesired behaviors. To many observers, myself included, these defenses have seemed like a particularly promising possible solution to AI safety and control problems, with at least some hope of achieving adversarial robustness, since presumably it would be hard to perform specific behaviors without passing through their natural cognitive precursor states.
However, through an extensive series of experiments targeting three major classes of latent space defenses, the authors demonstrate that it very often is possible, with a mix of methods that they collectively call obfuscation attacks, to elicit specific target behaviors while shifting internal activations so as to avoid detection. For example, for supervised probes, which learn from human labeled examples to identify the activation patterns associated with harmful inputs or outputs, they show how to maintain the specific target behavior while shifting activations so as to avoid detection. Similarly, for out of distribution detectors, which use statistical techniques to identify deviations from normal activity and have been used to detect backdoor attacks like anthropics famous sleeper agents.
They again develop methods that preserve the unwanted behavior while keeping activations within expected statistical bounds. And for sparse autoencoders, which of course can be used to understand the high level concepts that a model is working with at any given layer and token position, they again demonstrate that it is possible to produce a specific, desired or undesired behavior while avoiding detection at any specific layer in the model. Though performance does gradually degrade when they try to evade SAE detection at multiple layers. The amount of work and the number of experiments in this paper is remarkable, and there is a ton of detail in this conversation. We cover multiple different kinds of attacks, including prompt suffixes, soft prompts, and data poisoning.
The structure of the lost functions that they optimized against The level of access required for these attacks to work, and the fact that some of these attacks work even when the defender moves last. Some really fascinating cat and mouse type experiments that show attacks working over and over again over 70 rounds of back and forth between attack and defense. And even their attempts to make models easier to monitor through an approach that they call obfuscated adversarial training, which they found does make it harder, but still by no means impossible to perform these obfuscated activation attacks.
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Chapters
8 chapters
1
What are latent‑space defenses and why are they needed?
0:00–4:08
2
How do the researchers motivate the study of obfuscated activations?
4:08–8:03
3
What types of probes and detectors are used to monitor model behavior?
8:03–12:42
4
How is the dual‑objective loss function designed to both preserve behavior and fool detectors?
12:42–1:04:33
5
Why don’t latent‑space defenses need to know the exact trigger in advance?
1:04:33–1:19:04
6
What high‑level results did the authors observe about the strength of obfuscated attacks?
1:19:04–1:34:22
7
Is it practical to place a probe at every layer of a large language model?
1:34:22–1:48:39
8
How does obfuscated adversarial training change the relationship between activations and harmful behavior?
1:48:39–2:04:02
Speakers
2 identifiedMore from "The Cognitive Revolution"
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