CryptoDB
On the Insider Security of MLS
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Presentation: | Slides |
Conference: | CRYPTO 2022 |
Abstract: | The Messaging Layer Security (MLS) protocol is an open standard for end-to-end (E2E) secure group messaging being developed by the IETF, poised for deployment to consumers, industry, and government. It is designed to provide E2E privacy and authenticity for messages in long-lived sessions whenever possible, despite the participation (at times) of malicious insiders that can adaptively interact with the PKI at will, actively deviate from the protocol, leak honest parties' states, and fully control the network. The core of the MLS protocol (from which it inherits essentially all of its efficiency and security properties) is a Continuous Group Key Agreement (CGKA) protocol. It provides asynchronous E2E group management by allowing group members to agree on a fresh independent symmetric key after every change to the group's state (e.g. when someone joins/leaves the group). In this work, we make progress towards a precise understanding of the insider security of MLS (Draft 12). On the theory side, we overcome several subtleties to formulate the first notion of insider security for CGKA (or group messaging). Next, we isolate the core components of MLS to obtain a CGKA protocol we dub Insider Secure TreeKEM (ITK). Finally, we give a rigorous security proof for ITK. In particular, this work also initiates the study of insider secure CGKA and group messaging protocols. Along the way we give three new (very practical) attacks on MLS and corresponding fixes. (Those fixes have now been included into the standard.) We also describe a second attack against MLS-like CGKA protocols proven secure under all previously considered security notions (including those designed specifically to analyze MLS). These attacks highlight the pitfalls in simplifying security notions even in the name of tractability. |
Video from CRYPTO 2022
BibTeX
@inproceedings{crypto-2022-32160, title={On the Insider Security of MLS}, publisher={Springer-Verlag}, author={Joël Alwen and Daniel Jost and Marta Mularczyk}, year=2022 }