ElevenLabs

MITRE ATT&CK in Detective Settings: Avito Launches Cyberdetective Game for Security Awareness Training

Avito security awareness specialist Ilya Nikiforov has developed Cyberdetective, a browser-based detective game that trains employees against real-world attack techniques mapped to MITRE ATT&CK. The game places players in the fictional company Lumi where they control mascot Spock the honey badger, investigate six cases involving voice cloning, MFA fatigue, lookalike domains, and physical tailgating. Each scenario trains specific defensive habits such as callback verification and evidence preservation rather than simple rule recall. Development took 74 days with AI agents handling code, tests, art generation, voice synthesis, and video while the creator focused on scenario design and methodology. The project includes 21 additional training tasks on a firing range covering phishing, AI misuse, and supply-chain risks, supported by 2,200 automated tests and an editor system for future content updates. The initiative demonstrates how non-developers can build production-grade security tools using modern AI tooling while maintaining strict data-protection controls.

Why AI Detectors Cannot Be Trusted: The Shift to Watermarks and C2PA Standards

Detecting AI-generated images by examining fingers, teeth, or text has become ineffective as modern generators now produce realistic hands, photographic simulations, and synthetic voices. Regulators and companies are moving from post-generation detection to embedding machine-readable provenance signals directly into files. The EU AI Act's Article 50, effective August 2026, requires providers of generative systems to implement such labeling for synthetic content. Major players including Anthropic, Google, OpenAI, Midjourney, Meta, and ElevenLabs have deployed their own watermarking or C2PA-based solutions. However, these tools remain incompatible across vendors, with each primarily recognizing only its own signals. Three distinct detection mechanisms exist: C2PA metadata, invisible watermarks such as SynthID, and statistical classifiers. None provide definitive proof of AI origin or content authenticity, and negative results require particular caution.