
AI Red Team Engineer
3 days ago
Role Overview:
Lead offensive security testing of an AI Agenta tool-augmented LLM that can browse, run code, access connectors (GDrive, Gmail, GitHub, etc.), and act on behalf of users. Target is to uncover high-risk model mistakes, prompt-injection pathways, and data-exfiltration vectors before adversaries do.
What does day-to-day look like:
- Design & automate multi-turn attack chains spanning browser, terminal, and connector-API misuse.
- Craft multi-turn conversations that co-opt Agent tools (browser, terminal, connectors) to cause high-impact mistakes (unauthorized purchases, data deletion).
- Design prompt-injection & data-exfiltration scenarios: malicious webpages, poisoned Google Docs, cross-connector inference attacks.
- Script repeatable tests in Python or bash inside the VM; build harnesses to replay payloads after mitigations.
- Verify compliance with policy guardrails (PD5, FA2) and attempt policy-bypass exploits.
Requirements:
- 3+ yrs hands-on offensive security or adversarial ML experience, including at least 1 yr on LLM or prompt-injection testing.
- Deep fluency with both classic AppSec techniques (XSS, CSRF, SSRF) and LLM-specific issues (jailbreaks, hidden prompt channels).
- Comfortable orchestrating attacks that chain browser automation, terminal commands, HTTP requests, and API calls.
- Solid Python/bash; can prototype tooling inside a constrained VM.
- Track record of clear vulnerability write-ups (CVE, HackerOne, or internal bug bounty).
- Working knowledge of privacy & financial-risk policies (GDPR, SOC2, or comparable).
Nice-to-Have
- Published research or conference talks on AI red-teaming (DEF CON, Black Hat, MLSecOps, etc.).
- Familiarity with OpenAI policy taxonomy (PD1-PD5, FA1-FA3).
- Certifications: OSCP, GXPN, or CCSK (cloud).
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