F5 AI Red Team
Define the attack surface and command a swarm of agents designed to hunt and attack vulnerabilities in AI models, applications, and agents. Simulate adversarial attacks such as prompt injection and jailbreaks at unprecedented speed and scale, providing insight into threats both obvious and obscure.
Agentic intelligence for threats obvious and obscure
Threats to AI apps, models, and agents are growing exponentially. The most proactive AI red teaming will always benefit from a human defender, but the pace of AI development elevates the need for increased firepower. F5 AI Red Team empowers teams with a vast and continuously updated prompt database to test for vulnerabilities and streamline insights into implementation.
Outpace adversaries with F5 AI Red Team
Discover AI vulnerabilities
Identify and fix AI exploits before attackers find them
- Agent fingerprints: Trace complex attack patterns and pinpoint the exact execution paths used to manipulate deployed AI models
- Continuous assessment: Rapidly evaluate effectiveness of existing AI security controls and prioritize new controls by severity
Validate resilience and performance
Identify AI performance limits under real-world pressure
- Automate at scale: Replace manual testing with autonomous attacker agents across sophisticated, static, prompt-based, and multi-turn AI attacks
- Reduce failover states: Prevent downtime and keep models stable, secure, and scalable.
- Expose the unknown: Uncover obscure, hidden vulnerabilities in dynamic AI agentic workflows before they reach production
Remediate insights into threat-informed defense
Automate remediation of vulnerabilities into active guardrails
- Close the security loop: Instantly translate discovered vulnerabilities into AI guardrails
- Automate remediation: Push discovered threat reports to native AI guardrails to prevent zero-day exploits in minutes, not weeks
Expansive attack database
Test resilience against an expansive attack database and custom risk categories
- Leverage AI threat intel: Continuously test against thousands of real-world adversarial attack techniques, with over 10,000 attack patterns added every month
- Tailor by use case: Customize attack campaigns for specific intents through natural-language prompts
- Enable AI compliance: Align AI red teaming to compliance needs with purpose-built testing
Core capabilities
Agentic fingerprints
Gain attack-path insight into how and why agentic attacks succeed
Automated adversarial testing
Discover emergent risks with a swarm of agents trained on advanced techniques
Expansive attack database
Leverage the preeminent AI threat library of over 10,000 monthly attacks
AI remediate
Integrate with F5 AI Guardrails to automate insights into remediation
Trace and explain every successful attack path with F5 AI Red Team's agentic fingerprints
Deploy F5 AI Red Team with complete privacy and flexibility
Public cloud
Deploy F5 AI Red Team directly alongside AI workloads in AWS, Azure, or Google Cloud environments
On-premises
Deploy on premises with F5 AI Red Team, a certified Red Hat OpenShift operator
Resources
Demos and videos
Blogs
Webinars
Insights and reports
Case studies
Technology alliances
Stronger AI security with the partners you trust
F5 integrates with leading security and AI vendors to extend protection across the full lifecycle. Joint solutions secure data, models, and the APIs between them, keeping your AI fast, governed, and under your control.
Frequently asked questions
Manual AI red teaming uses human experts to creatively probe AI systems for vulnerabilities, while automated red teaming uses tools and scripts to systematically test at scale. The best approach combines both; automation for comprehensive coverage and human expertise for nuanced, creative attack scenarios.
Without adversarial testing, organizations risk prompt injection attacks, data leakage, jailbreaks, and unauthorized outputs that can expose sensitive data, damage brand reputation, and compromise intellectual property. The findings from adversarial testing can also help inform AI risk management and compliance programs aligned with frameworks such as the NIST AI Risk Management Framework (AI RMF) and EU AI Act.
AI red teaming specifically targets AI/ML models and their unique vulnerabilities, including prompt injection and AI-specific jailbreaks, whereas traditional penetration testing focuses on network, application, and infrastructure security flaws. It can also help security teams evaluate AI-specific risks reflected in frameworks such as the OWASP Top 10 for LLM Applications.
Red team cadences depend on unique risk definitions, as well as industry-specific and compliance needs. At a minimum, the pace of AI threat landscape shifts makes monthly red teaming a baseline standard, however, many teams conduct red team reports as a daily component of CI/CD pipelines.
At this time, F5 AI Red Team uses agents to execute attack campaigns, but the subject of those campaigns is a deployed model or application rather than an agent specifically. AI agents at their core are simply models capable of executing chained tasks through tool calls, so many of the vulnerabilities discovered for the model will also apply by extension to agentic workflows.
Open source frameworks such as Microsoft PyRIT can support custom AI red teaming workflows and security research. F5 AI Red Team adds automated adversarial testing at enterprise scale, with a continuously updated attack database, multi-turn testing, agentic fingerprints, recurring campaigns, reporting, and deployment options across cloud and on-premises environments.
F5 AI Red Team can test retrieval-augmented generation (RAG)–enabled applications for data leakage, prompt injection, and other adversarial weaknesses before they are exploited. Findings can then inform remediation and runtime controls, including F5 AI Guardrails, to help protect production AI interactions.
Prompt injection uses malicious instructions to manipulate a model or application into overriding intended behavior, including indirect prompt injection delivered through retrieved content or connected data. Jailbreaking is a form of adversarial prompting designed to bypass model safety controls or restrictions. AI red teaming tests both to identify where models, applications, and workflows can be manipulated before attackers exploit those weaknesses.