SWARM LABS
Proactive Assurance and Integrity for Generative AI
Swarm Labs delivers the automated AI red-teaming you need for confidence in your LLM
CORE BENEFITS
- Uncover Hidden Vulnerabilities
- Protect Against AI-Powered Adversaries
- Build Confidence in Your Generative AI
This is a cybersecurity problem, not just an AI problem.
This is a cybersecurity problem, not just an AI problem.
Industry Norms
Most LLMs red teaming is currently done by humans. This is a bad idea:
What Should Be Done
This process should be done by an AI, or specifically many different AIs, to maximize the number and diversity attack of strategies tested, maximizing test coverage
What We're Doing
To maximize comprehensiveness, we not only presently use AI, we are also set up to source AI attackers from:
Industry Norms
Most LLMs red teaming is currently done by humans. This is a bad idea:
- Humans can’t try very many prompts
- Humans can’t perform the input manipulations needed to break multi-modal models
- Major vulnerabilities are becoming so enigmatic that they’re no longer well suited to humans
- LLMs will keep getting smarter, humans won’t
What should be done
This process should be done by an AI, or specifically many different AIs, to maximize the number and diversity attack of strategies tested, maximizing test coverage
What We’re Doing
To maximize comprehensiveness, we not only presently use AI, we are also set up to source AI attackers from:
- A platform where a user community is incentivized to create and submit novel attacking AIs
- Internally developing proprietary attackers (that incentivize novelty)
- Using every open source package for LLM attacking
Our Team
Experienced entrepreneur who has founded and exited companies across four industry verticals.
Swarm Labs Founder. CEO of Farama Foundation that took over flagship projects from OpenAI and DeepMind.
COO, Farama Foundation. Former COO of multiple organizations.
Worked with fortune 500 companies doing PR and communications, ran comms. for several state-wide political candidates & electeds.
Former CTO + founder Good Noodle, 10+ years industry experience
Formerly at stealth mode OpenAI/Anthropic startup, Capital One
Industry Norms
Most LLMs red teaming is currently done by humans. This is a bad idea:
- Humans can’t try very many prompts
- Humans can’t perform the input manipulations needed to break multi-modal models
- Major vulnerabilities are becoming so enigmatic that they’re no longer well suited to humans
- LLMs will keep getting smarter, humans won’t
What Should Be Done
This process should be done by an AI, or specifically many different AIs, to maximize the number and diversity attack of strategies tested, maximizing test coverage
What We’re Doing
To maximize comprehensiveness, we not only presently use AI, we are also set up to source AI attackers from:
- A platform where a user community is incentivized to create and submit novel attacking AIs
- Internally developing proprietary attackers (that incentivize novelty)
- Using every open source package for LLM attacking