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Faster Than Light Speed Protocol (FLIP)
RFC 9564

Document Type RFC - Informational (April 2024) Errata
Last updated 2024-04-01
RFC stream Independent Submission
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Send notices to (None)
RFC 9564

Independent Submission                                       M. Blanchet
Request for Comments: 9564                                      Viagenie
Category: Informational                                     1 April 2024
ISSN: 2070-1721

                Faster Than Light Speed Protocol (FLIP)


   The recent advances in artificial intelligence (AI) such as large
   language models enable the design of the Faster than LIght speed
   Protocol (FLIP) for Internet.  FLIP provides a way to avoid
   congestion, enhance security, and deliver faster packets on the
   Internet by using AI to predict future packets at the receiving peer
   before they arrive.  This document describes the protocol, its
   various encapsulations, and some operational considerations.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This is a contribution to the RFC Series, independently of any other
   RFC stream.  The RFC Editor has chosen to publish this document at
   its discretion and makes no statement about its value for
   implementation or deployment.  Documents approved for publication by
   the RFC Editor are not candidates for any level of Internet Standard;
   see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at

Copyright Notice

   Copyright (c) 2024 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   ( in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.

Table of Contents

   1.  Introduction
   2.  Protocol Peer Preparation
   3.  FLIP Header
   4.  Protocol Operation
   5.  Versioning
   6.  Future Work
   7.  IANA Considerations
   8.  Security Considerations
   9.  Informative References
   Author's Address

1.  Introduction

   ChatGPT was introduced to the public on 30 November 2022 [CHATGPT].
   Since then, large language models (LLMs) have been used for a large
   variety of applications.  It demonstrates the powerful ability to
   generate precise output based on the input and based on the
   appropriate training of the LLM.  This protocol specification uses
   this ability to predict future packets before they arrive at the
   receiving peer, therefore achieving faster-than-light-speed delivery,
   hence the protocol name: Faster than LIght speed Protocol (FLIP).

   Since FLIP can predict packets, frames, strings, or byte streams, it
   could be used at any layer of the IP protocol stack.  Moreover, with
   proper training, FLIP can also predict future encrypted packets, as
   encryption is just strings of bytes.  This specification shows FLIP
   as a Layer 2 shim as well as a transport shim layer.  Since FLIP can
   be used at any layer, it is expected that additional specifications
   will be created, such as predicting HTTP requests and answers, email
   content, and more.

   Since communications in deep space are unfortunately limited to light
   speed, and given the very large distances between spacecrafts and
   Earth, the consequence is very long delays.  By offering faster-than-
   light-speed delivery, FLIP is a key enabler and addition to deep-
   space IP networking [IP-DEEP-SPACE].

2.  Protocol Peer Preparation

   In order to successfully achieve faster than light speed, the peers
   of any protocol layer used by FLIP must prepare their side of the
   connection with the right model trained for the specific case.  This
   document does not dictate any specific LLM, as the implementations
   may choose the one that best works for their use case and train them
   accordingly.  As with any LLM, it is paramount to use a lot of
   training data, such as packet captures, in a variety of conditions to
   produce the best trained model.  To avoid security, privacy, and
   legal issues, the specifics of which LLM is used, how it was trained,
   and what is the data set used, shall not be published nor disclosed
   in the protocol.

   As an example, an implementation may elect to collect a significant
   number of Packet Capture (PCAP) files from tcpdump wiretapping at
   various vantage points on the Internet.  The fact that traffic may be
   encrypted is not an issue, since a well-trained LLM will be able to
   predict encrypted traffic as accurately as unencrypted traffic.

3.  FLIP Header

   Wherever FLIP is used (below IP, above IP or other transport, or at
   the application layer), a FLIP shim header is inserted.

      |  Version | Command | Inner Protocol | Optional Data  |

   The header contains the following fields:

   Version:  A field of variable and unspecified length that contains
      the SHA-256 hash of the model, used as the version, as described
      in Section 5.

   Command:  The codepoint identifying the operation of this FLIP frame.
      Commands are described in Section 4.  The initial list of valid
      FLIP commands is below.

      The maximum number size is infinite, given that artificial
      intelligence peers can support an infinite number of commands, by
      just updating their models without the need to update their
      protocol implementation.

                     | Command | Codepoint | Reference |
                     | model   | 0x01      | RFC 9564  |
                     | data    | 0x02      | RFC 9564  |

                                   Table 1

   Inner Protocol:  As the FLIP header is a shim header, the inner
      protocol is specified in this field.  For example, for a FLIP shim
      header inserted between IP and TCP, the IP packet will contain the
      FLIP codepoint as the transport protocol.  The FLIP inner protocol
      field will then contain the TCP codepoint that would otherwise be
      in the IP packet.

   Optional Data:  Some commands have additional data that are following
      the Command field.

   The header length is variable and depends on which command is used.
   Given the use of artificial intelligence by implementations of this
   protocol, the actual length of the header, and the length of each of
   its fields, is not specified in the header.  Instead, it is expected
   that the proper neural network on the receiver side will be able to
   find the actual header termination, thus saving many header bits.

   To properly signal the upper layer about the presence of the FLIP
   header, a specific codepoint is reserved at the layer below FLIP.
   Section 7 lists the registrations for IP and transport codepoints for
   this use.

4.  Protocol Operation

   Prior to sending a first packet using FLIP, the sender and the
   receiver should be configured with the appropriate model trained as
   discussed before.  It is left to the implementation to choose the
   right LLM and the right training data set.

   The following commands are defined:

   Model:  (codepoint 0x01).  This command provides a way for peers to
      send their model in-band of the FLIP protocol.  The model itself
      is carried in the Optional Data field of the FLIP header.  Prior
      to the actual model data, a MIME header is inserted with the
      proper media type.  If the media type for the model does not
      exist, it should be registered in the IANA Media Type registry.

   Data:  (codepoint 0x02).  This command tells the receiving peer that
      the data that follows can be predicted and therefore achieves
      faster-than-light-speed performance.

   Sending the model in-band to the other peer is an operation that
   should be done rarely, as models may be large in size.  Moreover, it
   actually discloses the model for any wiretapping adversary.
   Implementors may consider using a post-quantum cryptographic
   algorithm that is also immune to AI prediction, therefore a post-
   Quantum-AI cryptographic algorithm.

5.  Versioning

   As described in [RFC6709], most protocols should be designed to
   enable future enhancements, such as providing a way to signal a new
   version of the protocol.  In the case of FLIP, trained models will
   always be enhanced by new training.  A SHA-256 [RFC6234] hash of the
   trained model is used as a version number so each peer knows which
   FLIP version is being used.  The SHA-256 hash is put in version field
   in the FLIP header as described previously.  Given that new SHA-256
   hashes are not sequential but fully random, replay attacks of future
   predictions are prevented.

6.  Future Work

   This new protocol may revolutionize how we design Internet protocols
   and how we use the Internet.  For example, it is envisioned that this
   protocol may be used for video streaming, augmented reality, virtual
   reality, and post-quantum cryptography to name a few.  By predicting
   the future packets, all these protocols and applications can benefit
   the use of FLIP.

7.  IANA Considerations

   For FLIP, codepoints could be registered in the following IANA

   *  Protocol Numbers [IANA-PN]: 345, FLIP, Faster than LIght speed
      Protocol, RFC 9564

   *  Service Name and Transport Protocol Port Number Registry
      [IANA-SN]: FLIP, 68534, udp and tcp, RFC 9564

8.  Security Considerations

   The ability to predict future packets based on LLMs can be used by
   adversaries that are listening to the traffic via wiretapping.  If
   they have access to the same model used by the destination peer, they
   could use it to predict the next packets and then initiate various
   attacks, including novel ones such as the "futureplay attack."
   Compared to the typical replay attack, this attack is where the
   adversary will predict future packets and then send them in advance
   to the destination.  While it may not be obvious at this time, these
   novel attacks should be investigated before they become a problem.
   Therefore, further research in this field is suggested.

   The ability for a peer to predict future packets enhances the overall
   security of the Internet because adversaries will not be able to
   inject bad packets in a connection, as the destination will be able
   to compare the received bad packet with the calculated prediction and
   therefore will easily identify and deny any bad packets.

9.  Informative References

   [CHATGPT]  Wikipedia, "ChatGPT", 20 March 2024,

   [IANA-PN]  IANA, "Protocol Numbers",

   [IANA-SN]  IANA, "Service Name and Transport Protocol Port Number
              Registry", <

              Blanchet, M., Huitema, C., and D. Bogdanović, "Revisiting
              the Use of the IP Protocol Stack in Deep Space: Assessment
              and Possible Solutions", Work in Progress, Internet-Draft,
              draft-many-deepspace-ip-assessment-01, 4 March 2024,

   [RFC6234]  Eastlake 3rd, D. and T. Hansen, "US Secure Hash Algorithms
              (SHA and SHA-based HMAC and HKDF)", RFC 6234,
              DOI 10.17487/RFC6234, May 2011,

   [RFC6709]  Carpenter, B., Aboba, B., Ed., and S. Cheshire, "Design
              Considerations for Protocol Extensions", RFC 6709,
              DOI 10.17487/RFC6709, September 2012,


   Since this protocol specification is using artificial intelligence
   and large language models, it was deemed that dumb humans must not
   review this specification.  Instead, the specification has been
   submitted to multiple LLM chat services and was enhanced by their
   comments and suggestions, hence acknowledged here.  In fact, this
   specification may have been produced entirely by LLM chat services.
   Moreover, given the specifications being produced by the IETF relying
   upon human intelligence, using LLMs to produce specifications should
   be envisioned.  Finally, given the difficulty to find experts for
   management positions such as in the IESG or IAB, the use of LLMs
   should be considered to replace those roles.  Unfortunately, given
   privacy, security, and legal considerations, the LLM chat services
   used for this specification cannot be named here.

Author's Address

   Marc Blanchet