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AI safety and security
takes a crowd

For organizations adopting AI, relying on traditional testing to find safety and security vulnerabilities can create its own risk. Instead, a crowdsourced approach to AI red teaming meets the challenges of the moment.

AI Security Solutions

Adopt AI with confidence

The adoption of LLM applications and other AI systems promises revolutionary competitive advantages, just as technologies like mobile apps, cloud computing, and IoT did in the past. However, as with any new technology wave, AI adds significant new vulnerabilities to the attack surface involving security, ethics, and behavior, with the risk often amplified by deep integration with other systems. Vulnerability types include:

  • Prompt Injection
  • LLM Sensitive Data Exposure
  • Excessive Agency
  • Data Bias

By minimizing these risks, AI adopters can move forward productively and with confidence.

What is AI security?

AI has three significant roles: tool, target, and threat

Both sides of the security battlefield will use AI systems to scale up their attacks/defenses. For example, threat actors may use content generator bots to create more convincing spear phishing attacks while security teams can train AI models to detect abnormal usage within milliseconds.


Threat actors will exploit vulnerabilities in companies’ AI systems. AI systems usually have access to data and other services, so threat actors will also potentially be able to breach such systems via the AI vector.


Some fear AI models could cause insidious harm. We’ve already seen incidents where LLM applications have reflected bias and hateful speech in their behavior due to their presence in training data.



Unleash hacker ingenuity to defend AI attack surface

Our multi-purpose platform for crowdsourced security meets the needs of the moment for AI adopters (just as it did for previous new technology waves), and helps meet regulatory requirements for AI red teaming and other security and safety standards described in Executive Order 14110, OMB Memorandum M-24-10, and the EU AI Act.


Uncover data bias and other hidden risks

By activating the expertise of 3rd-party security researchers at scale, incentivized crowdsourcing engagements like AI Bias Assessments and bug bounties can uncover data bias and other vulnerabilities that traditional testing will miss.


Run targeted AI penetration tests

AI Penetration Testing can deliver targeted, time-bound offensive testing to uncover hidden vulnerabilities in LLM and other AI applications. Bugcrowd will build a team with precisely the skills needed from our deep bench of trusted talent.


Create a “neighborhood watch” for AI risk

Standing up a vulnerability disclosure program gives the hacker/researcher community at large a formal channel for altruistically reporting flaws in LLMs applications and other AI systems, before threat actors can find them.

Ultimate Guide to AI Security

Ultimate Guide to AI Security

With AI use increasing rapidly and governments around the world implementing AI regulations, security and business leaders must make the effort to understand AI security immediately. This report covers everything you need to know to be prepared to bolster AI security in 2024.

Download Now



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Securing the New AI Attack Surface


AI Attack

“The OpenAI Bug Bounty Program is a way for us to recognize and reward the valuable insights of security researchers who contribute to keeping our technology and company secure.”
OpenAI Bug Bounty Program
“We are strongly committed to ensuring that the DoD’s AI-enabled systems—and the contexts in which they run—are safe, secure, reliable, and bias free.”
Department of Defense (DoD) Chief Digital and Artificial Intelligence Office (CDAO)
Practicing What We Preach

Get started with Bugcrowd

Hackers aren’t waiting, so why should you? See how Bugcrowd can quickly improve your security posture.