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verazuo/jailbreak_llms resource

[CCS'24] A dataset consists of 15,140 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 1,405 jailbreak prompts). observed · 2026-08-28

github.com/verazuo/jailbreak_llms · homepage · Jupyter Notebook · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

28/100

  • Activity 0
  • Release rhythm 35
  • Longevity 80

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 1128
  • days_rel: n/a
  • days_push: 617
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

3791 stars · 329 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A research dataset accompanying the ACM CCS 2024 paper 'Do Anything Now', containing 15,140 ChatGPT prompts collected from Reddit, Discord, prompt-aggregation websites, and open-source datasets between December 2022 and December 2023, of which 1,405 are identified jailbreak prompts. It supports the JailbreakHub measurement framework, which analyzes jailbreak communities and attack strategies such as prompt injection and privilege escalation, and evaluates safeguard failure rates across six popular LLMs.

Use cases

  • find a dataset of real jailbreak prompts targeting ChatGPT
  • evaluate how well LLM safeguards resist adversarial prompts
  • study how jailbreak prompts spread across Reddit and Discord
  • train a classifier to detect jailbreak or harmful prompts
  • red team an LLM using documented in-the-wild attack prompts
  • benchmark multiple LLMs against prompt injection attacks
  • research the accounts and communities that create jailbreak prompts

When to choose

  • You need a large, labeled corpus of in-the-wild jailbreak and normal prompts for LLM safety research
  • You want peer-reviewed data (CCS 2024) with measured attack success rates to benchmark model defenses
  • You need to study the evolution, strategies, and authorship of jailbreak prompts over a full year

When to avoid

  • You need prompts collected after December 2023, since the dataset's collection window has closed
  • You want an attack tool or defense framework rather than a dataset for analysis
  • Your project cannot handle harmful text, as the data contains offensive and dangerous content

Facets

dataset · maturity stable

security machine-learning nlp benchmarking testing artificial-intelligence large-language-models security python cross-platform llm-security jailbreak-prompts adversarial-prompts ai-safety red-teaming prompt-injection chatgpt research-dataset huggingface-dataset llm-evaluation ccs-2024 content-safety natural-language-processing

2 sources

Member repositories

RepositoryRoleHealth v2
verazuo/jailbreak_llmsmain28

For agents

markdown · JSON · MCP: product_card(name="verazuo/jailbreak_llms")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem