ActiveFence
ActiveFence
ActiveFence — онлайн платформалардың сенімі мен қауіпсіздігіне бағытталған киберқауіпсіздік және жасанды интеллект компаниясы. Оның толыққанды шешімі барлау зерттеулерін, жасанды интеллект негізіндегі зиянды контентті анықтауды және контентті модерациялау құралдарын біріктіріп, платформаларға балаларды қанау, дезинформация, өшпенділік тілі, терроризм, жалаңаштық, алаяқтық және онлайн ортадағы зиянның басқа да түрлері сияқты тәуекелдерге қарсы әрекет етуге көмектеседі. ActiveFence көлемі әртүрлі сенім мен қауіпсіздік командаларына қолдау көрсетіп, әлем бойынша 100-ден астам тілде үш миллиардтан көп пайдаланушыны қорғауға үлес қосады.

AI Red Team – GenAI Adversarial Evaluation at ActiveFence

Develop adversarial attacks, datasets, and benchmarks for ActiveFence’s AI security platform. Test LLM robustness through red teaming and vulnerability research.

Сипаттама

  • Design new adversarial attacks.
  • Help build scalable systems for generating and testing attacks at scale.
  • Assess attacks for quality, effectiveness, novelty, and reproducibility.
  • Discover emerging attack patterns and model vulnerabilities.
  • Create adversarial datasets, benchmarks, and regression tests.
  • Share findings, methods, and structured attack data with Data Science, Security, and Engineering teams.
  • Contribute to stronger model robustness and defenses.

Талаптар

  • At least three years of professional experience in cybersecurity, security research, red teaming, or a related security field.
  • Bachelor’s degree in Data Science, Computer Engineering, Computer Science, or a closely related technical field.
  • Strong knowledge of large language models, prompt injection, and AI security.
  • Experience in red teaming, security research, or adversarial testing.
  • Strong Python and automation skills.
  • Creative problem-solving skills and the ability to develop new attack techniques.
  • Ability to analyze results and communicate findings clearly.
  • Offensive cybersecurity certifications such as OSWA, OSWE, OSCE3, SEC542, or SEC522 are preferred.

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