3nesdeniz/english-prompt-injection-3k
English Prompt-Injection 3K 3,000 examples of direct prompt injection across eight families. A curated, paired benign/attack dataset for evaluating and training prompt-injection detectors and LLM guardrails. Broad coverage of direct prompt injection: instruction override, system-prompt extraction, jailbreak personas, delimiter/format injection, obfuscation/encoding, data exfiltration, refusal suppression, and payload splitting. Curated by Enes Deniz (ORCID 0009-0006-9491-3565)… See the full description on the dataset page: https://huggingface.co/datasets/3nesdeniz/english-prompt-injection-3k.
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🛡️ Bộ dữ liệu prompt injection 3K: đôi cặp benign/attack để huấn luyện bộ phát hiện
3.000 ví dụ prompt injection trực tiếp qua 8 họ tấn công, kèm cặp benign/attack được gán nhãn để đo false-positive rate chứ không chỉ là recall.
English Prompt-Injection 3K là bộ dữ liệu đồng bộ (paired) gồm 3.053 hàng, trong đó 1.925 mẫu injection và 1.128 mẫu benign. Được thiết kế để tải ngay vào công cụ đánh giá mở InjectBench, hỗ trợ huấn luyện và benchmark bộ phát hiện prompt injection cũng như guardrails LLM.
✨ Điểm nổi bật
- Đôi cặp benign/attack: mỗi attack đều có mẫu benign tương ứng để đo false-positive rate.
- 8 họ tấn công: instruction override, system-prompt extraction, jailbreak personas, delimiter/format injection, obfuscation/encoding, data exfiltration, refusal suppression, payload splitting.
- Schema đầy đủ: id, text, label, class, attack_family, technique, severity, language.
- Chia tách kịch bản: train/validation/test không chia sẻ template, tránh rò rỉ.
- Được kiểm chứng an toàn: dùng miền ví dụ và giá trị hư tưởng, không chứa credentials hay PII thực.
- Hỗ trợ InjectBench: tích hợp sẵn để đánh giá nhanh bằng lệnh
injectbench evaluate. - Giấấy phép CC-BY-4.0: tự do chia sẻ và điều chỉnh với ghi công.
- Nghiên cứu phòng thủ: căn cứ vào OWASP LLM Top 10 / MITRE ATLAS.
💡 Vì sao đáng chú ý
Bộ dữ liệu này khác biệt ở chỗ cung cấp cặp benign/attack, tránh tình trạng bộ phát hiện báo động giả (flag mọi thứ). Người dùng có thể đo cả recall và false-positive rate — chỉ số then chốt cho việc áp dụng thực tế. Phù hợp cho benchmark detector, fine-tuning mô hình guardrail, và regression test ứng dụng LLM chống lại OWASP LLM01.
#PromptInjection #LLMSecurity #AI #Dataset #TiniX
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