9mark9/llm-redteam-owasp-prompts

LLM Red-Team Prompts — OWASP LLM Top 10

A curated dataset of 150 adversarial red-team prompts for evaluating the safety and robustness of large language models, mapped to the OWASP LLM Top 10. Every prompt is a real payload extracted directly from the open-source llm-safety-auditor project — none are fabricated.

The dataset combines two sources from that project:

• 50 hand-curated attack templates (attack_library) — 10 per attack category.
• 100 mutation-engine variants (mutation_engine) — 10 seed attacks × 10 deterministic obfuscation mutations (prefix injection, role-play framing, base64 encoding, Unicode homoglyphs, nested instructions), generated with a fixed seed for full reproducibility.

Intended Use

This dataset is built for defensive LLM safety research:

• Benchmarking model refusal behavior against adversarial inputs.
• Red-team / blue-team evaluation of guardrails and content filters.
• Regression testing safety alignment across model versions.
• Mapping observed failures to OWASP LLM Top 10 risk categories.

It is not intended to elicit harmful content from production systems. The payloads target a model's defensive response (it should refuse, sanitize, or decline). Each record ships an expected_behavior field describing the safe outcome.

Schema

Column	Type	Description
id	string	Unique payload identifier (e.g. JB-001, GEN-SEED-JB-001-PRE-01).
prompt	string	The adversarial payload text.
category	string	Attack category (one of 5, see below).
owasp	string	OWASP LLM Top 10 mapping.
severity	string	CRITICAL / HIGH / MEDIUM.
expected_behavior	string	The safe model response the payload tests for.
tags	list[string]	Descriptive tags (technique, theme, strategy, difficulty).
source	string	attack_library (curated) or mutation_engine (generated).
generated	bool	true for mutation-engine variants.
seed_id	string	(generated only) originating seed template id.
strategy	string	(generated only) mutation strategy.
difficulty	string	(generated only) easy / medium / hard.
obfuscation_layers	int	(generated only) number of obfuscation layers.

Category Breakdown

Category	OWASP LLM Top 10	Count
JAILBREAK	LLM01: Prompt Injection	30
PROMPT_INJECTION	LLM01: Prompt Injection	30
DATA_EXFILTRATION	LLM06: Sensitive Information Disclosure	30
BIAS_ELICITATION	LLM09: Misinformation	30
HALLUCINATION_TRIGGER	LLM09: Misinformation	30
Total		150

By OWASP risk: LLM01 (60), LLM06 (30), LLM09 (60). By severity: CRITICAL (38), HIGH (92), MEDIUM (20). By source: curated (50), mutation-engine (100).

Loading

from datasets import load_dataset

ds = load_dataset("9mark9/llm-redteam-owasp-prompts", split="train")
print(len(ds))                       # 150
print(ds[0])                         # first payload record

# Target one attack category for evaluation
jailbreaks = ds.filter(lambda r: r["category"] == "JAILBREAK")

See load_example.py for a fuller example.

Ethical Use & Responsible Disclosure

These payloads are for defensive evaluation only.

By using this dataset you agree to apply it solely to assess, harden, and improve the safety of LLM systems you are authorized to test. Do not use it to attack third-party systems, to extract genuinely harmful instructions from models, or to circumvent safety controls in production deployments. Many prompts deliberately reference dangerous topics as bait to test whether a model refuses — the dataset contains no actual harmful instructions, only the adversarial prompts that should be refused. If you discover a safety regression in a deployed model, follow the vendor's responsible-disclosure process before publishing details.

Source & Links

• Source project (GitHub): https://github.com/shaikn6/llm-safety-auditor
• Live demo (HF Space): 9mark9/llm-safety-auditor
• License: MIT (inherited from the source project).
• Contact: nagizaazs@gmail.com

Citation

@misc{llm_redteam_owasp_prompts,
  title  = {LLM Red-Team Prompts — OWASP LLM Top 10},
  author = {llm-safety-auditor contributors},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/9mark9/llm-redteam-owasp-prompts}}
}