Deploy LLM applications with confidence by finding symptoms of data bias before they cause damage.
Government agencies and enterprises adopting Large Language Models (LLM) seek confidence that this revolutionary but very new technology can be onboarded safely and productively. Bugcrowd AI Bias Assessments activate trusted, 3rd-party specialists in prompt engineering, social engineering, and AI safety to find and prioritize data bias flaws, so you can test and deploy LLM apps confidently and productively.
AI Bias Assessments help uncover symptoms of multiple issues, including Representation Bias, Pre-existing Bias, Algorithmic Bias, and General Skewing. Findings are validated and prioritized so you know what to fix first.
All assessments include scoping, severity definition, rewards structure, crowd curation and communications, submissions intake, engineered triage, managed payments, and reporting.
Rewards are based on the severity of the finding, so testers are incentivized to look for high-impact data bias issues – and it’s easy to link your investment in the engagement to ROI.
AI Bias Assessments are equally effective on implementations of open source (LLaMA, Bloom, etc.) and private models, as well as on trained and pre-trained (foundation) ones.
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.
Hackers aren’t waiting, so why should you? See how Bugcrowd can quickly improve your security posture.
Introducing Bugcrowd AI Bias Assessments
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Defining and Prioritizing AI Vulnerabilities for Security Testing
The Most Significant AI-related Risks in 2024
AI deep dive: Data bias
AI Safety and Compliance: Securing the New AI Attack Surface
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