For organizations adopting AI, using traditional testing to find safety and security vulnerabilities can create its own risk. Instead, crowd-powered AI red teaming meets the challenges of the moment.
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:
By minimizing these risks through AI red teaming, AI adopters can move forward productively and with confidence.
What is AI security?
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.
IT TAKES A CROWD
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, as described in regulations such as the EU AI Act.
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.
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.
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.
With AI use increasing rapidly and governments around the world implementing AI regulations, security and AI teams must make the effort to understand AI safety and security immediately. This report covers everything you need to know to be prepared to bolster both in 2025.
Defining and Prioritizing AI Vulnerabilities for Security Testing
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The Most Significant AI-related Risks in 2024
AI deep dive: LLM jailbreaking
Cybersecurity and Generative AI Predictions with David Fairman, CIO and CSO of Netskope
AI Safety and Compliance: Securing the New AI Attack Surface
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The Promptfather: An Offer AI Can’t Refuse
AI security in 2024: What’s new?
AI deep dive: Data bias
Hackers aren’t waiting, so why should you? See how Bugcrowd can quickly improve your security posture.