Sr Staff Researcher (Agentic AI Systems Security...

We are looking for a security engineer to lead threat research and detection strategy for agentic AI systems, in a hands-on role split across research and engineering. You will model the threat surface across the full agentic stack - agents, skills, MCP servers, and traditional model artifacts, including their configurations and deployment architecture - for both proprietary hosted models and in-house custom deployments, then build that research into production scanning capabilities and own the services that ship them.

Core Technical Focus & Ownership

You bring systemic fluency in the agentic threat landscape: direct and indirect (cross-domain) prompt injection, jailbreak and guardrail bypass, instruction-hierarchy violations, and the agent-loop failures they enable - excessive agency, goal drift, and memory or RAG poisoning. That extends into the tool and extension layer: MCP tool poisoning, tool shadowing, server rug pulls, OAuth scope and token-passthrough abuse, and supply-chain risk in third-party skills and plugins. Deployment posture matters equally - exposed inference endpoints, least-privilege tool scoping and ephemeral credentials, insecure output handling, and egress control over the channels that turn a single injection into data loss. In production, you run the pipeline itself: service health, latency and availability targets, release quality, and false-positive/false-negative regression gates, with on-call response and observability as day-to-day work.

Qualifications

Impact

• Shape detection and scanning strategy by identifying the vulnerability, exploit, and attack-technique areas in the agentic-AI domain where new or improved protections are needed.

• Drive innovative detection ideas from concept to production, delivering measurable improvements in coverage, quality, speed, or scalability.

• Expand the team’s ability to deliver protections at scale through practical automation, detection pipeline improvements, and AI-assisted research workflows.

• Provide hands-on technical leadership to researchers and developers through direction, review, problem decomposition, and execution guidance.

• Influence cross-functional decisions with product, QA, engineering, and research partners to ensure detections are technically sound, customer-relevant, and production-ready.

• Support the operational excellence of the detection pipeline and other common infrastructure owned by the team.

• Deep hands-on experience in vulnerability research, exploit analysis, offensive security, or closely related threat prevention work.

• Deep and proven expertise using agentic-AI systems with an eye for finding loopholes, across both proprietary hosted models and in-house custom model deployments. Using agentic-AI practices as tooling and automation to improve security analysis, detection development, validation, or response workflows is highly desirable.

• Proven ability to identify important technical problems, propose detection ideas, drive execution, and deliver measurable product or customer impact, specifically in the agentic-AI threat research domain.

• Deep understanding of common vulnerability classes and exploit techniques - including memory corruption, injection, authentication bypass, path traversal, SSRF, RCE, XSS, SQL injection, CSRF, MITM, and DoS - and how these classes resurface through agent tool wrappers, tool parameters, and insecurely handled model output.

• Strong ability to analyze vulnerability root cause, exploitability, PoC behavior, network traffic, protocol behavior, application-layer attack patterns, and detection tradeoffs.

• Experience translating vulnerability or exploit understanding into production-quality scanning capabilities or other customer-facing protections.

• Strong programming or scripting skills for research automation, tooling, test generation, detection development, or pipeline improvements.

• Ability to lead complex technical work under ambiguity, guide other researchers or developers, and make sound technical decisions under time pressure.

• Strong communication skills with the ability to influence technical direction across research, product, QA, and engineering partners.

• BS/MS in Computer Science, Computer Engineering, Cybersecurity, or related field, or equivalent professional experience.

The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/com-missioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here (https://benefits.paloaltonetworks.com/) .

$139,600.00 - $225,775.00/yr

Our Commitment

We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together.

At Palo Alto Networks®, we’re united by a shared mission-to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.

Who We Are

In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us!

We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.