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ServiceNow is seeking a Staff AI Security Specialist to secure AI agents and agentic systems within ServiceNow's products and platform. As AI agents gain reasoning, context retrieval, and real-world action capabilities, they represent a new and largely unmapped attack surface. This applied role sits at the intersection of AI and security, focusing on building controls that don't yet exist.
In this position, you will:
- Analyze the security of AI agent systems, including how context is assembled, tool-calling loops function, agent planning and delegation, memory persistence, and responses to untrusted input, identifying adversarial subversion points
- Design and build security controls for agentic systems: static analysis of agent and tool configurations, runtime policy enforcement, behavioral detection, and mitigations for tool and goal hijacking
- Build working prototypes end-to-end, then partner with engineering teams to harden proven solutions
- Threat model new AI architectures and features before shipping, converting findings into concrete controls
- Red-team ServiceNow's own agents and defenses, including indirect prompt injection, tool abuse, privilege escalation across agent boundaries, and data exfiltration through model and tool channels
- Build repeatable evaluations and benchmarks to measure control effectiveness as models and products evolve
- Track offensive and defensive AI security research, quickly assessing relevance and roadmap impact
- Advise product and platform teams on secure agentic design and elevate collective AI security depth
- Publish research, patents, and external talks on findings
This is a hands-on role where you'll spend time reasoning about how agentic systems break, then writing code to stop it, collaborating closely with security engineers from prototype to shipped control.
REQUIREMENTS:
- 6+ years combined experience in security, software engineering, or applied research, including 2+ years hands-on with AI/ML or LLM-based systems (required)
- Strong working knowledge of agent harness and security-relevant seams: system prompt and context construction, tool-calling loop, context window management and compaction, sub-agent delegation, sandboxing and execution boundaries (required)
- Working knowledge of agent memory and retrieval systems—what persists, who can write to it, how it is read back—plus orchestration frameworks and tool protocols such as MCP and their failure modes (required)
- Demonstrated depth in at least one security domain (application security, offensive security, authorization and identity, or detection engineering), with instinct to reason in terms of attacker capability, preconditions, and attack chains (required)
- Practical understanding of AI-specific threat landscape: prompt, tool, and goal hijacking; indirect injection through retrieved content; memory poisoning and adversarial instruction persistence; insecure agent configurations; cross-session and cross-tenant data leakage; OWASP Top 10 for LLM Applications; MITRE ATLAS (required)
- Proficiency in Python, sufficient to independently build and ship working systems (required)
- Ability to take ambiguous problem statements to working artifacts, own them end-to-end, and abandon them without ceremony when evidence indicates failure (required)
- Ability to communicate technical concepts to both non-technical business users and technical stakeholders (required)
- Experience leveraging integration of AI capabilities into work processes, decision-making, or problem-solving (required)
PREFERRED:
- Experience building systems that make and enforce security decisions at runtime
- Experience building static or dynamic analysis tooling
- Experience with AI red-teaming, adversarial ML, or evaluating models and guardrails at scale
- Published security or AI research, patents, CVEs, or meaningful open-source contributions
