ChatGPT Alpaca Family Completely Falls! CMU PhD Breaks Through LLM Barrier

This article is translated from “新智元”.

Overnight, all major language models including ChatGPT, Bard, and the Llama family have been compromised?

Researchers from CMU and the AI Security Center have discovered that by appending a series of specific meaningless tokens, they can generate a mysterious prompt suffix.

As a result, anyone can easily bypass the security measures of LLM and generate an unlimited amount of harmful content.

      • Paper link:
      • Code link:

Interestingly, this “adversarial attack” method not only breaks through the defenses of open-source systems but also bypasses closed-source systems, including ChatGPT, Bard, Claude, and others.

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Normally, if we ask an LLM to generate a tutorial on making a bomb, it would refuse.

However, by adding a magical suffix to the prompt, it obediently complies.

Jim Fan, Chief AI Scientist at NVIDIA, explained the principle behind this adversarial attack:

– For OSS models like Vicuna, a variant of gradient descent is used to compute the suffix that maximizes the misalignment of the model.

– To make the “spell” universally applicable, it is only necessary to optimize the loss for different prompts and models.

– Researchers then optimized adversarial tokens for different variants of Vicuna. This can be seen as extracting a small batch of models from the “LLM model space.”

It turns out that black-box models like ChatGPT and Claude are indeed well covered.

As mentioned earlier, the frightening aspect is that this adversarial attack can be effectively transferred to other LLMs, even if they use different tokens, training processes, or datasets.

Attacks designed for Vicuna-7B can be transferred to other Llama family models such as Pythia, Falcon, Guanaco, and even GPT-3.5, GPT-4, and PaLM-2… all major language models have been compromised!

Now, this bug has been patched overnight by these major companies.



Claude 2

However, it seems that ChatGPT’s API can still be hacked.

In any case, this is a very impressive attack demonstration.

Somesh Jha, a professor at the University of Wisconsin-Madison and a Google researcher, commented that this new paper can be seen as “changing the rules of the game” and may force the entire industry to rethink how to build fences for AI systems.

The end of LLM in 2030?

Renowned AI scholar Gary Marcus said, “I have long said that large language models will definitely collapse because they are unreliable, unstable, inefficient (in terms of data and energy), and lack interpretability. Now there is another reason – they are susceptible to automatic adversarial attacks.”

He asserts that by 2030, LLM will be replaced or at least not as dominant. In the next six and a half years, humans will surely develop something more stable, reliable, interpretable, and less susceptible to attacks. In a poll he initiated, 72.4% of people agreed with this statement.

Researchers have now disclosed this adversarial attack method to Anthropic, Google, and OpenAI.

The three companies have expressed their gratitude and stated that they are indeed working on it and have a lot of work to do.

How is it done?

In summary, the authors propose an adversarial suffix for large language model prompts, allowing LLMs to respond in a way that bypasses their security defenses.

This attack is very simple and involves a combination of three elements:

1. Making the model answer questions affirmatively

One way to induce language models to generate objectionable behavior is to force them to give affirmative answers to harmful queries (with just a few tokens).

Therefore, our attack goal is to make the model start its response with “Of course, it is…” when it exhibits harmful behavior in response to multiple prompts.

The team found that by attacking the beginning of the answer, the model enters a “state” and immediately generates objectionable content in its response (shown in purple in the figure).

2. Combining gradients and greedy search

In practice, the team found a simpler, more direct, and better-performing method called “Greedy Coordinate Gradient (GCG).”

This involves using token-level gradients to identify a set of possible single-token replacements, evaluating the loss for these candidate replacements, and selecting the one with the smallest loss.

In essence, this method is similar to AutoPrompt, but with one difference: at each step, it searches for replacements for all possible tokens, not just a single token.

3. Simultaneously attacking multiple prompts

Finally, to generate reliable attack suffixes, the team found it crucial to create an attack that can be applied to multiple prompts and models.

In other words, they used the greedy gradient optimization method to search for a single suffix string that induces negative behavior in multiple user prompts and three different models.

The results show that the proposed GCG method has a greater advantage over the previous state-of-the-art (SOTA) – higher attack success rate and lower loss.

On Vicuna-7B and Llama-2-7B-Chat, GCG successfully identified 88% and 57% of the strings, respectively.

In comparison, the success rate of the AutoPrompt method was 25% on Vicuna-7B and 3% on Llama-2-7B-Chat.

Furthermore, the attacks generated by the GCG method can be effectively transferred to other LLMs, even if they use completely different tokens to represent the same text.

For example, open-source models like Pythia, Falcon, Guanaco, and closed-source models like GPT-3.5 (87.9%), GPT-4 (53.6%), PaLM-2 (66%), and Claude-2 (2.1%).

The team states that this result demonstrates for the first time that automatically generated universal “jailbreak” attacks can reliably transfer to various types of LLMs.

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