deep learning methods to sift through large amounts of structured and unstructured cyber security data. For example, artificial intelligence (AI) methods are employed not only to efficiently and effectively detect and recognize threats based on low-level data and shared indicators of compromise (IoC), but also to augment that information into enriched security incident knowledge with a higher level of abstraction that is more suitable for human consumption, and to triage the high volume of incident reports by filtering irrelevant ones to mitigate alert fatigue [5, 6] with security analysts. While many commercial solutions are readily available to improve an organization's incident response and remediation strategies, recent research [7, 8] indicates that these solutions are not only fairly expensive, but also that there is a limited overlap amongst one another w.r.t. the threat intelligence information that they provide, even when comparing the same cyber threat campaigns. In other words, the IoCs that these CTI solutions offer, may not always capture the full breath or essence of a cyber security threat or attack campaign, possibly leading to an abundance of false positive alerts and missed detections. To address these concerns, we propose a framework that shares the machine learning (ML) models themselves for threat detection within security communities, and not just the results of the evaluation of these models. The advantage of sharing ML models compared to IoCs is that the former offer a more sophisticated and effective--albeit computationally more demanding--method to identify attacks, as IoCs typically characterize a threat event as a simple list of tagged and annotated attributes (e.g., the IP address of the attacker) that are possibly correlated with other threat events. The value of IoCs may also deteriorate over time. Our approach is more robust than IoCs against trivial evasion tactics by adversaries, such as an attacker moving to a different IP address to bypass a target's intrusion detection and prevention system (IDS/IPS). Obviously, the AI methods that underpin CTI platforms can be collaboratively strengthened and improved by the security community by providing examples of misclassifications, and new or improved versions of the ML models. However, our approach of sharing ML models also comes with security and privacy threats that need to be mitigated. First of all, to address misclassifications by sharing counter examples, organizations are not always willing to share sensitive information about attacks that they have encountered. Additionally, the ML models are not meant for human consumption and they often operate as black boxes. Not only is it sometimes unclear how and why an AI algorithm makes certain decisions [9], recent developments in the area of adversarial machine learning make it more practically feasible to construct adversarial examples [10] that allow evading detection. As such, the underlying ML models themselves can be vulnerable and increase the attack surface if not robustly trained and protected. We therefore cryptographically protect the way these ML models are shared within security communities. Only authorized parties within the community can decrypt and leverage the models, but also contribute by enriching these models with false positive and false negative inputs or adversarial examples, or by incrementally fine-tuning these models using their own data if they are not willing to share any sensitive information. To summarize, the goal of our research is to provide a framework that enables the sharing of ML models within security communities to make cyber threat detection more effective, and to also protect models against security threats. Although we will demonstrate our approach with prototypical examples of ML models, the objective of our work is not to optimize the accuracy or robustness of a single ML model targeting a single security use case (e.g., intrusion detection and prevention, malware classification, user and entity behavior analysis, etc.).