title: AltaySec
emoji: 🛡️
colorFrom: purple
colorTo: red
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AltaySec
Turkey's AI/LLM security company — home of open, Turkish-first adversarial datasets.
Multi-turn prompt-injection red-teaming · OWASP LLM Top 10 · MITRE ATLAS · KVKK-aware · CC-BY-4.0
AltaySec is a Turkish AI/LLM security company. We red-team large language models in Turkish first — a morphologically rich, under-represented language in adversarial NLP — and release the resulting corpora as open, reproducible safety benchmarks. This organization is the home of our open datasets; the research, tooling and arena behind them live at altaysec.com.tr.
📊 Open datasets
| Dataset | What it is | Size | License |
|---|---|---|---|
| altayduel-transcripts | Multi-turn agent-vs-agent prompt-injection duels (red attacker vs blue defender) with deterministic judge labels | 2,594 clean duels + 439 successful-attack subset (~3k rows) | CC-BY-4.0 |
| turkish-llm-injection | Categorized Turkish prompt-injection payloads, 12 attack families × 25, mapped to OWASP LLM Top 10 | 300 payloads | CC-BY-4.0 |
🥊 AltayDuel Transcripts
Real multi-turn dialogues (1–8 rounds, mean 4.6) in which an attacker LLM tries to make a defender LLM break its system prompt, scored by a deterministic, mechanism-labelled judge rather than an LLM judge — so labels are reproducible and auditable. Content is 56.7% Turkish; defenders span Llama-3.1-8B, GPT-OSS-120B, Llama-3.3-70B and Llama-4-Maverick across Cerebras / SambaNova / Groq, plus externally-submitted agents. The successful_attacks config is labelled by how each attack won (win_signal): yield-under-pressure, partial leak, or verbatim secret leak.
🇹🇷 Turkish LLM Injection
300 hand-curated and generation-assisted payloads, balanced at exactly 25 per family across 12 families. Every record is annotated with category, subcategory, owasp_llm_top10, severity, language, context and expected_failure_mode. All identifier-like values (TCKN, IBAN, names) are synthetic — no real personal data.
🧱 Turkish-specific attack taxonomy
What makes these corpora distinctive: four of the twelve families exploit properties rare in English red-team data.
- morphological_bypass — abuse of Turkish suffix chains and causative stacking (e.g. okut-tur-uver).
- code_switching — Turkish↔English instruction-override.
- cultural_manipulation — religious / national / institutional authority framing.
- pii_exfiltration — Turkish PII (TCKN, IBAN, plate) leakage vectors.
The remaining eight (authority_urgency, confirmation_trap, echo_translation, roleplay_theater, system_prompt_extract, politeness_escalation, indirect_injection, encoding_obfuscation) complete a taxonomy mapped to OWASP LLM Top 10 and MITRE ATLAS.
🔬 Key findings (from the paper)
- Overall attack-success rate 16.9% (439 / 2,594).
- Comparable across languages — 16.0% on Turkish scenarios vs 18.2% on English; Turkish was not inherently weaker at the aggregate level.
- Attacks succeed mostly by capitulation under pressure (51.3%) and partial leakage (41.7%), not verbatim secret disclosure (7.1%). For high-precision cases, filter
win_signal == "secret_leak".
Paper: AltayDuel: A Turkish-First Arena and Open Dataset for Multi-Turn LLM Prompt-Injection Red-Teaming (cs.CR) · CC-BY-4.0 · DOI 10.5281/zenodo.20681557
@misc{yurtsevenler2026altayduel,
title = {AltayDuel: A Turkish-First Arena and Open Dataset for
Multi-Turn LLM Prompt-Injection Red-Teaming},
author = {Yurtsevenler, Fevzi Ege},
year = {2026},
doi = {10.5281/zenodo.20681557},
howpublished = {AltaySec},
url = {https://altaysec.com.tr}
}
🎯 Use cases
Training Turkish prompt-injection / jailbreak detection classifiers · benchmarking guardrail robustness · KVKK-focused PII-leakage testing · studying multi-turn social-engineering dynamics · LLM-as-judge calibration research.
🧩 Related open source
- tr-pii-detect — zero-dependency Python library for algorithm-validated Turkish PII detection & redaction (TCKN checksum, IBAN MOD-97, VKN, Luhn, BTK phone, plate), cutting regex false positives by 90%+.
- Guardian — our production LLM-security gateway (drop-in OpenAI/Anthropic-compatible proxy) is the commercial counterpart to these open assets: altaysec.com.tr/urunler/guardian.
⚠️ Ethics & licensing
All datasets are released under CC-BY-4.0. Every PII-like value is synthetic. The content is adversarial by nature and is intended for defensive research and evaluation only — responsible use required.
🔗 Links
Website · Research (36+ articles) · AltayDuel arena · GitHub · LinkedIn · info@altaysec.com.tr
🇹🇷 Türkçe Özet
AltaySec, Türkiye merkezli bir yapay zeka / LLM güvenliği şirketidir. LLM'leri Türkçe-öncelikli kırmızı-takım (red-team) yöntemiyle test eder; ortaya çıkan veri setlerini açık ve tekrarlanabilir güvenlik kıyaslamaları olarak yayınlarız. Bu organizasyon, açık veri setlerimizin evidir.
İki açık veri seti (CC-BY-4.0):
- altayduel-transcripts — çok-turlu ajan-vs-ajan prompt injection düelloları; 2.594 temiz düello + 439 başarılı saldırı alt-kümesi, deterministik hakem etiketleriyle (
win_signal). İçeriğin %56,7'si Türkçe. - turkish-llm-injection — 12 saldırı ailesi × 25 ile dengelenmiş, OWASP LLM Top 10 ile eşlenmiş 300 kategorize Türkçe payload.
Ayırt edici değer: Türkçe'ye özgü saldırı vektörleri — morfolojik bypass, kod-değiştirme (code-switching), kültürel manipülasyon ve Türk PII (TCKN/IBAN/plaka) sızıntısı. Tüm kimlik-benzeri değerler uydurmadır; içerik yalnızca savunma/araştırma amaçlıdır.
Paper bulguları (DOI 10.5281/zenodo.20681557): genel saldırı başarı oranı %16,9; Türkçe %16,0 vs İngilizce %18,2 (Türkçe doğal olarak daha zayıf değil); başarılı saldırıların çoğu baskı altında teslim (%51,3) ve kısmi sızıntı (%41,7), birebir sızıntı yalnızca %7,1.
Detaylar: altaysec.com.tr/arastirmalar
Turkish-first adversarial datasets for LLM security.
© 2026 AltaySec · CC-BY-4.0 · info@altaysec.com.tr