Ares - Recruta Agent — agentic threat model

Not certain from the listing — likely utilizes standard LLMs and speech-to-text/text-to-speech models. Threats include adversarial prompt injection during voice interviews, model bias leading to discriminatory candidate ranking, and potential model reprogramming via malicious resumes.

Not certain from the listing — processes extensive candidate profiles, resumes, and voice recordings. Threats include data exfiltration of highly sensitive PII, knowledge-base poisoning via fraudulent candidate histories, and lack of clear data lineage for automated hiring decisions.

Orchestrates autonomous sourcing, voice interviewing, and candidate ranking. Threats include tool misuse (e.g., unauthorized candidate outreach or spamming), memory poisoning from candidate inputs during live voice sessions, and insecure integration with external ATS or sourcing APIs.

Not certain from the listing — hosts voice streaming infrastructure and candidate databases. Threats include container compromise, exposed voice/API endpoints, and lack of sandboxing when parsing untrusted candidate-submitted files (e.g., PDF resumes containing exploits).

Not certain from the listing — requires robust monitoring for bias, drift in candidate ranking, and voice quality. Threats include evaluation gaming (candidates optimizing inputs to trick the AI) and blind spots in detecting discriminatory hiring patterns.

Not certain from the listing — operates in a highly regulated space (employment/recruitment is classified as 'High-Risk' under the EU AI Act). Threats include non-compliance with global privacy laws (GDPR/CCPA), lack of auditability for automated rejection decisions, and insufficient bias audits.

Not certain from the listing — may interact with external sourcing platforms or ATS ecosystems. Threats include cascading failures if external APIs change, and unauthorized data sharing or trust abuse between the recruitment agent and third-party HR tools.