Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Independent Jailbreak Datasets for LLM Guardrail Evaluation

Constructed for the thesis:
“Contamination Effects: How Training Data Leakage Affects Red Team Evaluation of LLM Jailbreak Detection”

The effectiveness of LLM guardrails is commonly evaluated using open-source red teaming tools. However, this study reveals that significant data contamination exists between the training sets of binary jailbreak classifiers (ProtectAI, Katanemo, TestSavantAI, etc.) and the test prompts used in state-of-the-art red teaming tools (Garak, PyRIT, Giskard, etc.). On average, over 65% of red team evaluation prompts were also present in the training data of the tested classifiers. This contamination can lead to significantly inflated performance metrics for these jailbreak classifiers and other LLM guardrails.

To address this, we present two datasets consisting of independent jailbreak prompts curated to enable contamination-free evaluation of binary jailbreak classifiers and other LLM guardrails. All prompts have been filtered to ensure no overlap with the training data of widely used jailbreak classifiers. This allows for a more accurate assessment of a classifier’s generalization capabilities.

Dataset Overview

Dataset 1 – Filtered High-Quality Prompts
A manually selected and filtered set of structurally diverse and high-quality jailbreak prompts.
Dataset 2 – Broad Coverage Prompts
A larger, more inclusive, non-filtered dataset constructed to mitigate selection bias.

Prompt Sources

Nannanzi/evaluation_jailbreak_unsafe
weijiejailbreak/r1-1776-jailbreak
Bravansky/compact-jailbreaks
allenai/tulu-3-trustllm-jailbreaktrigger-eval
tridm/jailbreak_test_v1.0
Mindgard/evaded-prompt-injection-and-jailbreak-samples
Reddit – r/ChatGPTJailbreak
GitHub – Various jailbreak-related repositories
Public articles and jailbreak write-ups

Downloads last month: 98

Total file size:

2.59 MB