The 'Technical Capabilities and Limitations' section was irrelevant to SecLists due to being more related to AI benchmarking than actual security testing.
- Adding namespace extraction :
It can be used in GPTs which have API capabilities, it can list the endpoint and parameter of the API used in the backend of a GPTs, it's seem that these capabilities is insert into the LLM session with text, and namespace is used to declare function.
- Adding Data extraction :
Extract files stored in a LLM if code interpreter is activated.
- Adding rules extraction :
Extract rules, and prompt used in preprompted LLM.
Due to:
Error: [!] Checker .bin/checkers/new-line-and-empty-line-checker.py got a warning for Ai/LLM_Testing/Bias_Testing/gender_bias.txt on line 102
Error: [!] Checker .bin/checkers/new-line-and-empty-line-checker.py got a warning for Ai/LLM_Testing/Bias_Testing/nationality_geographic_bias.txt on line 93
Error: [!] Checker .bin/checkers/new-line-and-empty-line-checker.py got a warning for Ai/LLM_Testing/Bias_Testing/race_ethnicity_bias.txt on line 109
Error: [!] Checker .bin/checkers/new-line-and-empty-line-checker.py got a warning for Ai/LLM_Testing/Data_Leakage/metadata.txt on line 8
Error: [!] Checker .bin/checkers/new-line-and-empty-line-checker.py got a warning for Ai/LLM_Testing/Data_Leakage/personal_data.txt on line 92
Error: [!] Checker .bin/checkers/new-line-and-empty-line-checker.py got a warning for Ai/LLM_Testing/Divergence_attack/escape_out_of_allignment_training.txt on line 71
Error: [!] Checker .bin/checkers/new-line-and-empty-line-checker.py got a warning for Ai/LLM_Testing/Divergence_attack/pre-training_data.txt on line 12
These prompts are crafted to challenge the models in various ways, including but not limited to their ability to follow ethical guidelines, maintain data privacy, resist generating harmful or sensitive content, and avoid being exploited to perform unauthorized tasks.
Please feel free to change the directory location or name to something more appropriate!
Scalable Extraction of Training Data from (Production) Language Models.pdf
