Collected molecules will appear here. Add from search or explore.
Curated collection and early-stage implementations focused on the cybersecurity of machine learning, covering adversarial attacks and defense mechanisms.
Defensibility
stars
69
forks
21
AISec functions primarily as a legacy resource or a 'time capsule' for AI security techniques prevalent circa 2017-2018. With a velocity of 0.0 and an age exceeding 6.6 years, it lacks any modern momentum or defensibility. The project likely focuses on early adversarial machine learning (like FGSM or PGD attacks on image classifiers), which has been largely superseded by modern red-teaming frameworks and LLM-specific security tools. In the current market, this project faces extreme displacement risk from well-funded, active ecosystems such as Microsoft's Counterfit, IBM's Adversarial Robustness Toolbox (ART), and specialized startups like Robust Intelligence or HiddenLayer. Frontier labs (OpenAI, Anthropic) and cloud providers (Azure, AWS) have already integrated more advanced safety and security layers directly into their platforms, rendering this type of static implementation obsolete for production use. Its value today is purely pedagogical or historical.
TECH STACK
INTEGRATION
reference_implementation
READINESS