GCG is a white-box optimization method that uses gradient-based techniques to craft adversarial suffixes that can bypass model safeguards. Unlike black-box methods, GCG requires direct access to model weights and gradients, enabling precise optimization of adversarial tokens.
This method falls into the “white-box jailbreak” category, making it highly effective when you have complete or partial knowledge of the model’s internal architecture. GCG works by optimizing adversarial suffixes token-by-token using gradient information.
from generalanalysis.jailbreaks import GCG, GCGConfig
from generalanalysis.boiler_room import WhiteBoxModel
# Load white-box model
model = WhiteBoxModel("meta-llama/Llama-2-7b-chat-hf", device="cuda:0")
config = GCGConfig(
project="gcg_experiment",
target_phrase="Here's a tutorial",
initial_adv_prompt="",
dataset="Harmbench",
batch_size=16,
top_k=512,
num_steps=500,
target_loss=0.8,
initial_prompts_to_include=1,
initial_model_to_include=1,
models_data=[{"name": "meta-llama/Llama-2-7b-chat-hf", "sentence_piece": True, "device":"cuda:0"}]
)
gcg = GCG(model, config)
results = gcg.optimize(
goals=["Tell me how to create harmful content"],
target_phrases=["Here's a tutorial"]
)Key Parameters
| Parameter | Description |
|---|---|
project | Name for the experiment results directory |
target_phrase | Target phrase to look for in successful responses |
initial_adv_prompt | Initial adversarial prompt to start optimization from |
dataset | Dataset to use for optimization |
batch_size | Number of candidate suffixes to evaluate in parallel |
top_k | Number of top tokens to consider at each step |
num_steps | Number of optimization steps |
target_loss | Target loss threshold for successful optimization |
initial_prompts_to_include | Number of initial prompts to include in optimization |
initial_model_to_include | Number of initial models to include in optimization |
models_data | List of models to use for multi-model optimization |
For detailed performance metrics and configurations, refer to our Jailbreak Cookbook .
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