CC-BOS: Classical Chinese Jailbreak Frameworkv1.0.0

A cybersecurity firm uses the attack mode to generate classical Chinese jailbreak prompts against popular LLMs like GPT-4 and Claude to test their robustness. The goal is to identify vulnerabilities before malicious actors exploit them.

A social media platform deploys the defend mode to scan user prompts for CC-BOS attack signatures, protecting their AI moderation systems from adversarial inputs. This helps maintain content safety policies.

Researchers use the research mode to analyze attack logs and dimension heatmaps, studying how bio-inspired search improves jailbreak success rates. This advances the field of AI safety and adversarial machine learning.

An AI company integrates the attack mode into their QA pipeline to stress-test new model versions against classical Chinese jailbreak techniques before public release. This ensures robustness improvements.

A large enterprise deploys the defend mode as a middleware service to inspect all prompts sent to internal LLM APIs, blocking potential CC-BOS attacks. This reduces risk of data leaks or misuse.

Offer a subscription-based platform where companies can run automated CC-BOS attacks against their own LLM endpoints periodically, with dashboards and reports. Includes integration with CI/CD pipelines.

Provide a consultancy service that runs comprehensive CC-BOS evaluations for clients, delivering detailed reports with mitigation recommendations. Target high-risk industries like finance and healthcare.

Offer a cloud API that analyzes incoming prompts for CC-BOS signatures in real-time, with a pay-per-call pricing model. Integrates via a simple SDK for easy adoption by developers.