    Internet Architecture Board (IAB)                              R. Barnes
    Request for Comments: 7624                                   B. Schneier
    Category: Informational                                      C. Jennings
    ISSN: 2070-1721                                                T. Hardie
                                                                 B. Trammell
                                                                  C. Huitema
                                                                 D. Borkmann
                                                                 August 2015

             Confidentiality in the Face of Pervasive Surveillance:
                      A Threat Model and Problem Statement

    Abstract

       Since the initial revelations of pervasive surveillance in 2013,
       several classes of attacks on Internet communications have been
       discovered.  In this document, we develop a threat model that
       describes these attacks on Internet confidentiality.  We assume an
       attacker that is interested in undetected, indiscriminate
       eavesdropping.  The threat model is based on published, verified
       attacks.

    Status of This Memo

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

       This document is a product of the Internet Architecture Board (IAB)
       and represents information that the IAB has deemed valuable to
       provide for permanent record.  It represents the consensus of the
       Internet Architecture Board (IAB).  Documents approved for
       publication by the IAB are not a candidate for any level of Internet
       Standard; see Section 2 of RFC 5741.

       Information about the current status of this document, any errata,
       and how to provide feedback on it may be obtained at
       http://www.rfc-editor.org/info/rfc7624.

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    Copyright Notice

       Copyright (c) 2015 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
       (http://trustee.ietf.org/license-info) 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  . . . . . . . . . . . . . . . . . . . . . . . .   3
       2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
       3.  An Idealized Passive Pervasive Attacker . . . . . . . . . . .   5
         3.1.  Information Subject to Direct Observation . . . . . . . .   6
         3.2.  Information Useful for Inference  . . . . . . . . . . . .   6
         3.3.  An Illustration of an Ideal Passive Pervasive Attack  . .   7
           3.3.1.  Analysis of IP Headers  . . . . . . . . . . . . . . .   7
           3.3.2.  Correlation of IP Addresses to User Identities  . . .   8
           3.3.3.  Monitoring Messaging Clients for IP Address
                   Correlation . . . . . . . . . . . . . . . . . . . . .   9
           3.3.4.  Retrieving IP Addresses from Mail Headers . . . . . .   9
           3.3.5.  Tracking Address Usage with Web Cookies . . . . . . .  10
           3.3.6.  Graph-Based Approaches to Address Correlation . . . .  10
           3.3.7.  Tracking of Link-Layer Identifiers  . . . . . . . . .  10
       4.  Reported Instances of Large-Scale Attacks . . . . . . . . . .  11
       5.  Threat Model  . . . . . . . . . . . . . . . . . . . . . . . .  13
         5.1.  Attacker Capabilities . . . . . . . . . . . . . . . . . .  14
         5.2.  Attacker Costs  . . . . . . . . . . . . . . . . . . . . .  17
       6.  Security Considerations . . . . . . . . . . . . . . . . . . .  19
       7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  20
         7.1.  Normative References  . . . . . . . . . . . . . . . . . .  20
         7.2.  Informative References  . . . . . . . . . . . . . . . . .  20
       IAB Members at the Time of Approval . . . . . . . . . . . . . . .  23
       Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  24
       Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  24

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    1.  Introduction

       Starting in June 2013, documents released to the press by Edward
       Snowden have revealed several operations undertaken by intelligence
       agencies to exploit Internet communications for intelligence
       purposes.  These attacks were largely based on protocol
       vulnerabilities that were already known to exist.  The attacks were
       nonetheless striking in their pervasive nature, in terms of both the
       volume of Internet traffic targeted and the diversity of attack
       techniques employed.

       To ensure that the Internet can be trusted by users, it is necessary
       for the Internet technical community to address the vulnerabilities
       exploited in these attacks [RFC7258].  The goal of this document is
       to describe more precisely the threats posed by these pervasive
       attacks, and based on those threats, lay out the problems that need
       to be solved in order to secure the Internet in the face of those
       threats.

       The remainder of this document is structured as follows.  In
       Section 3, we describe an idealized passive pervasive attacker, one
       which could completely undetectably compromise communications at
       Internet scale.  In Section 4, we provide a brief summary of some
       attacks that have been disclosed, and use these to expand the assumed
       capabilities of our idealized attacker.  Note that we do not attempt
       to describe all possible attacks, but focus on those that result in
       undetected eavesdropping.  Section 5 describes a threat model based
       on these attacks, focusing on classes of attack that have not been a
       focus of Internet engineering to date.

    2.  Terminology

       This document makes extensive use of standard security and privacy
       terminology; see [RFC4949] and [RFC6973].  Terms used from [RFC6973]
       include Eavesdropper, Observer, Initiator, Intermediary, Recipient,
       Attack (in a privacy context), Correlation, Fingerprint, Traffic
       Analysis, and Identifiability (and related terms).  In addition, we
       use a few terms that are specific to the attacks discussed in this
       document.  Note especially that "passive" and "active" below do not
       refer to the effort used to mount the attack; a "passive attack" is
       any attack that accesses a flow but does not modify it, while an
       "active attack" is any attack that modifies a flow.  Some passive
       attacks involve active interception and modifications of devices,
       rather than simple access to the medium.  The introduced terms are:

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       Pervasive Attack:  An attack on Internet communications that makes
          use of access at a large number of points in the network, or
          otherwise provides the attacker with access to a large amount of
          Internet traffic; see [RFC7258].

       Passive Pervasive Attack:  An eavesdropping attack undertaken by a
          pervasive attacker, in which the packets in a traffic stream
          between two endpoints are intercepted, but in which the attacker
          does not modify the packets in the traffic stream between two
          endpoints, modify the treatment of packets in the traffic stream
          (e.g., delay, routing), or add or remove packets in the traffic
          stream.  Passive pervasive attacks are undetectable from the
          endpoints.  Equivalent to passive wiretapping as defined in
          [RFC4949]; we use an alternate term here since the methods
          employed are wider than those implied by the word "wiretapping",
          including the active compromise of intermediate systems.

       Active Pervasive Attack:  An attack that is undertaken by a pervasive
          attacker and, in addition to the elements of a passive pervasive
          attack, also includes modification, addition, or removal of
          packets in a traffic stream, or modification of treatment of
          packets in the traffic stream.  Active pervasive attacks provide
          more capabilities to the attacker at the risk of possible
          detection at the endpoints.  Equivalent to active wiretapping as
          defined in [RFC4949].

       Observation:  Information collected directly from communications by
          an eavesdropper or observer.  For example, the knowledge that
          <alice@example.com> sent a message to <bob@example.com> via SMTP
          taken from the headers of an observed SMTP message would be an
          observation.

       Inference:  Information derived from analysis of information
          collected directly from communications by an eavesdropper or
          observer.  For example, the knowledge that a given web page was
          accessed by a given IP address, by comparing the size in octets of
          measured network flow records to fingerprints derived from known
          sizes of linked resources on the web servers involved, would be an
          inference.

       Collaborator:  An entity that is a legitimate participant in a
          communication, and provides information about that communication
          to an attacker.  Collaborators may either deliberately or
          unwittingly cooperate with the attacker, in the latter case
          because the attacker has subverted the collaborator through
          technical, social, or other means.

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       Key Exfiltration:  The transmission of cryptographic keying material
          for an encrypted communication from a collaborator, deliberately
          or unwittingly, to an attacker.

       Content Exfiltration:  The transmission of the content of a
          communication from a collaborator, deliberately or unwittingly, to
          an attacker

    3.  An Idealized Passive Pervasive Attacker

       In considering the threat posed by pervasive surveillance, we begin
       by defining an idealized passive pervasive attacker.  While this
       attacker is less capable than those that we now know to have
       compromised the Internet from press reports, as elaborated in
       Section 4, it does set a lower bound on the capabilities of an
       attacker interested in indiscriminate passive surveillance while
       interested in remaining undetectable.  We note that, prior to the
       Snowden revelations in 2013, the assumptions of attacker capability
       presented here would be considered on the border of paranoia outside
       the network security community.

       Our idealized attacker is an indiscriminate eavesdropper that is on
       an Internet-attached computer network and:

       o  can observe every packet of all communications at any hop in any
          network path between an initiator and a recipient;

       o  can observe data at rest in any intermediate system between the
          endpoints controlled by the initiator and recipient; and

       o  can share information with other such attackers; but

       o  takes no other action with respect to these communications (i.e.,
          blocking, modification, injection, etc.).

       The techniques available to our ideal attacker are direct observation
       and inference.  Direct observation involves taking information
       directly from eavesdropped communications, such as URLs identifying
       content or email addresses identifying individuals from application-
       layer headers.  Inference, on the other hand, involves analyzing
       observed information to derive new information, such as searching for
       application or behavioral fingerprints in observed traffic to derive
       information about the observed individual.  The use of encryption is
       generally sufficient to provide confidentiality by preventing direct
       observation of content, assuming of course, uncompromised encryption
       implementations and cryptographic keying material.  However,
       encryption provides less complete protection against inference,

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       especially inferences based only on plaintext portions of
       communications, such as IP and TCP headers for TLS-protected traffic
       [RFC5246].

    3.1.  Information Subject to Direct Observation

       Protocols that do not encrypt their payload make the entire content
       of the communication available to the idealized attacker along their
       path.  Following the advice in [RFC3365], most such protocols have a
       secure variant that encrypts the payload for confidentiality, and
       these secure variants are seeing ever-wider deployment.  A noteworthy
       exception is DNS [RFC1035], as DNSSEC [RFC4033] does not have
       confidentiality as a requirement.

       This implies that, in the absence of changes to the protocol as
       presently under development in the IETF's DNS Private Exchange
       (DPRIVE) working group [DPRIVE], all DNS queries and answers
       generated by the activities of any protocol are available to the
       attacker.

       When store-and-forward protocols are used (e.g., SMTP [RFC5321]),
       intermediaries leave this data subject to observation by an attacker
       that has compromised these intermediaries, unless the data is
       encrypted end-to-end by the application-layer protocol or the
       implementation uses an encrypted store for this data.

    3.2.  Information Useful for Inference

       Inference is information extracted from later analysis of an observed
       or eavesdropped communication, and/or correlation of observed or
       eavesdropped information with information available from other
       sources.  Indeed, most useful inference performed by the attacker
       falls under the rubric of correlation.  The simplest example of this
       is the observation of DNS queries and answers from and to a source
       and correlating those with IP addresses with which that source
       communicates.  This can give access to information otherwise not
       available from encrypted application payloads (e.g., the "Host:"
       HTTP/1.1 request header when HTTP is used with TLS).

       Protocols that encrypt their payload using an application- or
       transport-layer encryption scheme (e.g., TLS) still expose all the
       information in their network- and transport-layer headers to the
       attacker, including source and destination addresses and ports.
       IPsec Encapsulating Security Payload (ESP) [RFC4303] further encrypts
       the transport-layer headers but still leaves IP address information
       unencrypted; in tunnel mode, these addresses correspond to the tunnel
       endpoints.  Features of the security protocols themselves, e.g., the
       TLS session identifier, may leak information that can be used for

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       correlation and inference.  While this information is much less
       semantically rich than the application payload, it can still be
       useful for inferring an individual's activities.

       Inference can also leverage information obtained from sources other
       than direct traffic observation.  Geolocation databases, for example,
       have been developed that map IP addresses to a location, in order to
       provide location-aware services such as targeted advertising.  This
       location information is often of sufficient resolution that it can be
       used to draw further inferences toward identifying or profiling an
       individual.

       Social media provide another source of more or less publicly
       accessible information.  This information can be extremely
       semantically rich, including information about an individual's
       location, associations with other individuals and groups, and
       activities.  Further, this information is generally contributed and
       curated voluntarily by the individuals themselves: it represents
       information that the individuals are not necessarily interested in
       protecting for privacy reasons.  However, correlation of this social
       networking data with information available from direct observation of
       network traffic allows the creation of a much richer picture of an
       individual's activities than either alone.

       We note with some alarm that there is little that can be done at
       protocol design time to limit such correlation by the attacker, and
       that the existence of such data sources in many cases greatly
       complicates the problem of protecting privacy by hardening protocols
       alone.

    3.3.  An Illustration of an Ideal Passive Pervasive Attack

       To illustrate how capable the idealized attacker is even given its
       limitations, we explore the non-anonymity of encrypted IP traffic in
       this section.  Here, we examine in detail some inference techniques
       for associating a set of addresses with an individual, in order to
       illustrate the difficulty of defending communications against our
       idealized attacker.  Here, the basic problem is that information
       radiated even from protocols that have no obvious connection with
       personal data can be correlated with other information that can paint
       a very rich behavioral picture; it only takes one unprotected link in
       the chain to associate with an identity.

    3.3.1.  Analysis of IP Headers

       Internet traffic can be monitored by tapping Internet links or by
       installing monitoring tools in Internet routers.  Of course, a single
       link or a single router only provides access to a fraction of the

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       global Internet traffic.  However, monitoring a number of high-
       capacity links or a set of routers placed at strategic locations
       provides access to a good sampling of Internet traffic.

       Tools like the IP Flow Information Export (IPFIX) Protocol [RFC7011]
       allow administrators to acquire statistics about sequences of packets
       with some common properties that pass through a network device.  The
       most common set of properties used in flow measurement is the "five-
       tuple" of source and destination addresses, protocol type, and source
       and destination ports.  These statistics are commonly used for
       network engineering but could certainly be used for other purposes.

       Let's assume for a moment that IP addresses can be correlated to
       specific services or specific users.  Analysis of the sequences of
       packets will quickly reveal which users use what services, and also
       which users engage in peer-to-peer connections with other users.
       Analysis of traffic variations over time can be used to detect
       increased activity by particular users or, in the case of peer-to-
       peer connections, increased activity within groups of users.

    3.3.2.  Correlation of IP Addresses to User Identities

       The correlation of IP addresses with specific users can be done in
       various ways.  For example, tools like reverse DNS lookup can be used
       to retrieve the DNS names of servers.  Since the addresses of servers
       tend to be quite stable and since servers are relatively less
       numerous than users, an attacker could easily maintain its own copy
       of the DNS for well-known or popular servers to accelerate such
       lookups.

       On the other hand, the reverse lookup of IP addresses of users is
       generally less informative.  For example, a lookup of the address
       currently used by one author's home network returns a name of the
       form "c-192-000-002-033.hsd1.wa.comcast.net".  This particular type
       of reverse DNS lookup generally reveals only coarse-grained location
       or provider information, equivalent to that available from
       geolocation databases.

       In many jurisdictions, Internet Service Providers (ISPs) are required
       to provide identification on a case-by-case basis of the "owner" of a
       specific IP address for law enforcement purposes.  This is a
       reasonably expedient process for targeted investigations, but
       pervasive surveillance requires something more efficient.  This
       provides an incentive for the attacker to secure the cooperation of
       the ISP in order to automate this correlation.

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    3.3.3.  Monitoring Messaging Clients for IP Address Correlation

       Even if the ISP does not cooperate, user identity can often be
       obtained via inference.  POP3 [RFC1939] and IMAP [RFC3501] are used
       to retrieve mail from mail servers, while a variant of SMTP is used
       to submit messages through mail servers.  IMAP connections originate
       from the client, and typically start with an authentication exchange
       in which the client proves its identity by answering a password
       challenge.  The same holds for the SIP protocol [RFC3261] and many
       instant messaging services operating over the Internet using
       proprietary protocols.

       The username is directly observable if any of these protocols operate
       in cleartext; the username can then be directly associated with the
       source address.

    3.3.4.  Retrieving IP Addresses from Mail Headers

       SMTP [RFC5321] requires that each successive SMTP relay adds a
       "Received" header to the mail headers.  The purpose of these headers
       is to enable audit of mail transmission, and perhaps to distinguish
       between regular mail and spam.  Here is an extract from the headers
       of a message recently received from the perpass mailing list:

       Received: from 192-000-002-044.zone13.example.org (HELO
       ?192.168.1.100?) (xxx.xxx.xxx.xxx) by lvps192-000-002-219.example.net
       with ESMTPSA (DHE-RSA-AES256-SHA encrypted, authenticated); 27 Oct
       2013 21:47:14 +0100 Message-ID: <526D7BD2.7070908@example.org> Date:
       Sun, 27 Oct 2013 20:47:14 +0000 From: Some One <some.one@example.org>

       This is the first "Received" header attached to the message by the
       first SMTP relay; for privacy reasons, the field values have been
       anonymized.  We learn here that the message was submitted by "Some
       One" on October 27, from a host behind a NAT (192.168.1.100)
       [RFC1918] that used the IP address 192.0.2.44.  The information
       remained in the message and is accessible by all recipients of the
       perpass mailing list, or indeed by any attacker that sees at least
       one copy of the message.

       An attacker that can observe sufficient email traffic can regularly
       update the mapping between public IP addresses and individual email
       identities.  Even if the SMTP traffic was encrypted on submission and
       relaying, the attacker can still receive a copy of public mailing
       lists like perpass.

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    3.3.5.  Tracking Address Usage with Web Cookies

       Many web sites only encrypt a small fraction of their transactions.
       A popular pattern is to use HTTPS for the login information, and then
       use a "cookie" to associate following cleartext transactions with the
       user's identity.  Cookies are also used by various advertisement
       services to quickly identify the users and serve them with
       "personalized" advertisements.  Such cookies are particularly useful
       if the advertisement services want to keep tracking the user across
       multiple sessions that may use different IP addresses.

       As cookies are sent in cleartext, an attacker can build a database
       that associates cookies to IP addresses for non-HTTPS traffic.  If
       the IP address is already identified, the cookie can be linked to the
       user identify.  After that, if the same cookie appears on a new IP
       address, the new IP address can be immediately associated with the
       predetermined identity.

    3.3.6.  Graph-Based Approaches to Address Correlation

       An attacker can track traffic from an IP address not yet associated
       with an individual to various public services (e.g., web sites, mail
       servers, game servers) and exploit patterns in the observed traffic
       to correlate this address with other addresses that show similar
       patterns.  For example, any two addresses that show connections to
       the same IMAP or webmail services, the same set of favorite web
       sites, and game servers at similar times of day may be associated
       with the same individual.  Correlated addresses can then be tied to
       an individual through one of the techniques above, walking the
       "network graph" to expand the set of attributable traffic.

    3.3.7.  Tracking of Link-Layer Identifiers

       Moving back down the stack, technologies like Ethernet or Wi-Fi use
       MAC (Media Access Control) addresses to identify link-level
       destinations.  MAC addresses assigned according to IEEE 802 standards
       are globally unique identifiers for the device.  If the link is
       publicly accessible, an attacker can eavesdrop and perform tracking.
       For example, the attacker can track the wireless traffic at publicly
       accessible Wi-Fi networks.  Simple devices can monitor the traffic
       and reveal which MAC addresses are present.  Also, devices do not
       need to be connected to a network to expose link-layer identifiers.
       Active service discovery always discloses the MAC address of the
       user, and sometimes the Service Set Identifiers (SSIDs) of previously
       visited networks.  For instance, certain techniques such as the use
       of "hidden SSIDs" require the mobile device to broadcast the network
       identifier together with the device identifier.  This combination can
       further expose the user to inference attacks, as more information can

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       be derived from the combination of MAC address, SSID being probed,
       time, and current location.  For example, a user actively probing for
       a semi-unique SSID on a flight out of a certain city can imply that
       the user is no longer at the physical location of the corresponding
       AP.  Given that large-scale databases of the MAC addresses of
       wireless access points for geolocation purposes have been known to
       exist for some time, the attacker could easily build a database that
       maps link-layer identifiers and time with device or user identities,
       and use it to track the movement of devices and of their owners.  On
       the other hand, if the network does not use some form of Wi-Fi
       encryption, or if the attacker can access the decrypted traffic, the
       analysis will also provide the correlation between link-layer
       identifiers such as MAC addresses and IP addresses.  Additional
       monitoring using techniques exposed in the previous sections will
       reveal the correlation between MAC addresses, IP addresses, and user
       identity.  For instance, similarly to the use of web cookies, MAC
       addresses provide identity information that can be used to associate
       a user to different IP addresses.

    4.  Reported Instances of Large-Scale Attacks

       The situation in reality is more bleak than that suggested by an
       analysis of our idealized attacker.  Through revelations of sensitive
       documents in several media outlets, the Internet community has been
       made aware of several intelligence activities conducted by US and UK
       national intelligence agencies, particularly the US National Security
       Agency (NSA) and the UK Government Communications Headquarters
       (GCHQ).  These documents have revealed methods that these agencies
       use to attack Internet applications and obtain sensitive user
       information.  There is little reason to suppose that only the US or
       UK governments are involved in these sorts of activities; the
       examples are just ones that were disclosed.  We note that these
       reports are primarily useful as an illustration of the types of
       capabilities fielded by pervasive attackers as of the date of the
       Snowden leaks in 2013.

       First, they confirm the deployment of large-scale passive collection
       of Internet traffic, which confirms the existence of pervasive
       passive attackers with at least the capabilities of our idealized
       attacker.  For example, as described in [pass1], [pass2], [pass3],
       and [pass4]:

       o  NSA's XKEYSCORE system accesses data from multiple access points
          and searches for "selectors" such as email addresses, at the scale
          of tens of terabytes of data per day.

       o  GCHQ's Tempora system appears to have access to around 1,500 major
          cables passing through the UK.

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       o  NSA's MUSCULAR program has tapped cables between data centers
          belonging to major service providers.

       o  Several programs appear to perform wide-scale collection of
          cookies in web traffic and location data from location-aware
          portable devices such as smartphones.

       However, the capabilities described by these reports go beyond those
       of our idealized attacker.  They include the compromise of
       cryptographic protocols, including decryption of TLS-protected
       Internet sessions [dec1] [dec2] [dec3].  For example, the NSA BULLRUN
       project worked to undermine encryption through multiple approaches,
       including covert modifications to cryptographic software on end
       systems.

       Reported capabilities include the direct compromise of intermediate
       systems and arrangements with service providers for bulk data and
       metadata access [dir1] [dir2] [dir3], bypassing the need to capture
       traffic on the wire.  For example, the NSA PRISM program provides the
       agency with access to many types of user data (e.g., email, chat,
       VoIP).

       The reported capabilities also include elements of active pervasive
       attack, including:

       o  Insertion of devices as a man-in-the-middle of Internet
          transactions [TOR1] [TOR2].  For example, NSA's QUANTUM system
          appears to use several different techniques to hijack HTTP
          connections, ranging from DNS response injection to HTTP 302
          redirects.

       o  Use of implants on end systems to undermine security and anonymity
          features [dec2] [TOR1] [TOR2].  For example, QUANTUM is used to
          direct users to a FOXACID server, which in turn delivers an
          implant to compromise browsers of Tor users.

       o  Use of implants on network elements from many major equipment
          providers, including Cisco, Juniper, Huawei, Dell, and HP, as
          provided by the NSA's Advanced Network Technology group
          [spiegel1].

       o  Use of botnet-scale collections of compromised hosts [spiegel2].

       The scale of the compromise extends beyond the network to include
       subversion of the technical standards process itself.  For example,
       there is suspicion that NSA modifications to the DUAL_EC_DRBG random
       number generator (RNG) were made to ensure that keys generated using
       that generator could be predicted by NSA.  This RNG was made part of

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       NIST's SP 800-90A, for which NIST acknowledges the NSA's assistance.
       There have also been reports that the NSA paid RSA Security for a
       related contract with the result that the curve became the default in
       the RSA BSAFE product line.

       We use the term "pervasive attack" [RFC7258] to collectively describe
       these operations.  The term "pervasive" is used because the attacks
       are designed to indiscriminately gather as much data as possible and
       to apply selective analysis on targets after the fact.  This means
       that all, or nearly all, Internet communications are targets for
       these attacks.  To achieve this scale, the attacks are physically
       pervasive; they affect a large number of Internet communications.
       They are pervasive in content, consuming and exploiting any
       information revealed by the protocol.  And they are pervasive in
       technology, exploiting many different vulnerabilities in many
       different protocols.

       Again, it's important to note that, although the attacks mentioned
       above were executed by the NSA and GCHQ, there are many other
       organizations that can mount pervasive surveillance attacks.  Because
       of the resources required to achieve pervasive scale, these attacks
       are most commonly undertaken by nation-state actors.  For example,
       the Chinese Internet filtering system known as the "Great Firewall of
       China" uses several techniques that are similar to the QUANTUM
       program and that have a high degree of pervasiveness with regard to
       the Internet in China.  Therefore, legal restrictions in any one
       jurisdiction on pervasive monitoring activities cannot eliminate the
       risk of pervasive attack to the Internet as a whole.

    5.  Threat Model

       Given these disclosures, we must consider a broader threat model.

       Pervasive surveillance aims to collect information across a large
       number of Internet communications, analyzing the collected
       communications to identify information of interest within individual
       communications, or inferring information from correlated
       communications.  This analysis sometimes benefits from decryption of
       encrypted communications and deanonymization of anonymized
       communications.  As a result, these attackers desire both access to
       the bulk of Internet traffic and to the keying material required to
       decrypt any traffic that has been encrypted.  Even if keys are not
       available, note that the presence of a communication and the fact
       that it is encrypted may both be inputs to an analysis, even if the
       attacker cannot decrypt the communication.

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       The attacks listed above highlight new avenues both for access to
       traffic and for access to relevant encryption keys.  They further
       indicate that the scale of surveillance is sufficient to provide a
       general capability to cross-correlate communications, a threat not
       previously thought to be relevant at the scale of the Internet.

    5.1.  Attacker Capabilities

        +--------------------------+-------------------------------------+
        | Attack Class             | Capability                          |
        +--------------------------+-------------------------------------+
        | Passive observation      | Directly capture data in transit    |
        |                          |                                     |
        | Passive inference        | Infer from reduced/encrypted data   |
        |                          |                                     |
        | Active                   | Manipulate / inject data in transit |
        |                          |                                     |
        | Static key exfiltration  | Obtain key material once / rarely   |
        |                          |                                     |
        | Dynamic key exfiltration | Obtain per-session key material     |
        |                          |                                     |
        | Content exfiltration     | Access data at rest                 |
        +--------------------------+-------------------------------------+

       Security analyses of Internet protocols commonly consider two classes
       of attacker: passive pervasive attackers, who can simply listen in on
       communications as they transit the network, and active pervasive
       attackers, who can modify or delete packets in addition to simply
       collecting them.

       In the context of pervasive passive surveillance, these attacks take
       on an even greater significance.  In the past, these attackers were
       often assumed to operate near the edge of the network, where attacks
       can be simpler.  For example, in some LANs, it is simple for any node
       to engage in passive listening to other nodes' traffic or inject
       packets to accomplish active pervasive attacks.  However, as we now
       know, both passive and active pervasive attacks are undertaken by
       pervasive attackers closer to the core of the network, greatly
       expanding the scope and capability of the attacker.

       Eavesdropping and observation at a larger scale make passive
       inference attacks easier to carry out: a passive pervasive attacker
       with access to a large portion of the Internet can analyze collected
       traffic to create a much more detailed view of individual behavior
       than an attacker that collects at a single point.  Even the usual
       claim that encryption defeats passive pervasive attackers is
       weakened, since a pervasive flow access attacker can infer
       relationships from correlations over large numbers of sessions, e.g.,

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       pairing encrypted sessions with unencrypted sessions from the same
       host, or performing traffic fingerprinting between known and unknown
       encrypted sessions.  Reports on the NSA XKEYSCORE system would
       indicate it is an example of such an attacker.

       An active pervasive attacker likewise has capabilities beyond those
       of a localized active attacker.  Flow modification attacks are often
       limited by network topology, for example, by a requirement that the
       attacker be able to see a targeted session as well as inject packets
       into it.  A pervasive flow modification attacker with access at
       multiple points within the core of the Internet is able to overcome
       these topological limitations and perform attacks over a much broader
       scope.  Being positioned in the core of the network rather than the
       edge can also enable an active pervasive attacker to reroute targeted
       traffic, amplifying the ability to perform both eavesdropping and
       traffic injection.  Active pervasive attackers can also benefit from
       passive pervasive collection to identify vulnerable hosts.

       While not directly related to pervasiveness, attackers that are in a
       position to mount an active pervasive attack are also often in a
       position to subvert authentication, a traditional protection against
       such attacks.  Authentication in the Internet is often achieved via
       trusted third-party authorities such as the Certificate Authorities
       (CAs) that provide web sites with authentication credentials.  An
       attacker with sufficient resources may also be able to induce an
       authority to grant credentials for an identity of the attacker's
       choosing.  If the parties to a communication will trust multiple
       authorities to certify a specific identity, this attack may be
       mounted by suborning any one of the authorities (the proverbial
       "weakest link").  Subversion of authorities in this way can allow an
       active attack to succeed in spite of an authentication check.

       Beyond these three classes (observation, inference, and active),
       reports on the BULLRUN effort to defeat encryption and the PRISM
       effort to obtain data from service providers suggest three more
       classes of attack:

       o  Static key exfiltration

       o  Dynamic key exfiltration

       o  Content exfiltration

       These attacks all rely on a collaborator providing the attacker with
       some information, either keys or data.  These attacks have not
       traditionally been considered in scope for the Security
       Considerations sections of IETF protocols, as they occur outside the
       protocol.

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       The term "key exfiltration" refers to the transfer of keying material
       for an encrypted communication from the collaborator to the attacker.
       By "static", we mean that the transfer of keys happens once or rarely
       and that the transferred key is typically long-lived.  For example,
       this case would cover a web site operator that provides the private
       key corresponding to its HTTPS certificate to an intelligence agency.

       "Dynamic" key exfiltration, by contrast, refers to attacks in which
       the collaborator delivers keying material to the attacker frequently,
       e.g., on a per-session basis.  This does not necessarily imply
       frequent communications with the attacker; the transfer of keying
       material may be virtual.  For example, if an endpoint were modified
       in such a way that the attacker could predict the state of its
       pseudorandom number generator, then the attacker would be able to
       derive per-session keys even without per-session communications.

       Finally, content exfiltration is the attack in which the collaborator
       simply provides the attacker with the desired data or metadata.
       Unlike the key exfiltration cases, this attack does not require the
       attacker to capture the desired data as it flows through the network.
       The exfiltration is of data at rest, rather than data in transit.
       This increases the scope of data that the attacker can obtain, since
       the attacker can access historical data -- the attacker does not have
       to be listening at the time the communication happens.

       Exfiltration attacks can be accomplished via attacks against one of
       the parties to a communication, i.e., by the attacker stealing the
       keys or content rather than the party providing them willingly.  In
       these cases, the party may not be aware, at least at a human level,
       that they are collaborating.  Rather, the subverted technical assets
       are "collaborating" with the attacker (by providing keys/content)
       without their owner's knowledge or consent.

       Any party that has access to encryption keys or unencrypted data can
       be a collaborator.  While collaborators are typically the endpoints
       of a communication (with encryption securing the links),
       intermediaries in an unencrypted communication can also facilitate
       content exfiltration attacks as collaborators by providing the
       attacker access to those communications.  For example, documents
       describing the NSA PRISM program claim that NSA is able to access
       user data directly from servers, where it is stored unencrypted.  In
       these cases, the operator of the server would be a collaborator, if
       an unwitting one.  By contrast, in the NSA MUSCULAR program, a set of
       collaborators enabled attackers to access the cables connecting data
       centers used by service providers such as Google and Yahoo.  Because
       communications among these data centers were not encrypted, the
       collaboration by an intermediate entity allowed the NSA to collect
       unencrypted user data.

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    5.2.  Attacker Costs

         +--------------------------+-----------------------------------+
         | Attack Class             | Cost / Risk to Attacker           |
         +--------------------------+-----------------------------------+
         | Passive observation      | Passive data access               |
         |                          |                                   |
         | Passive inference        | Passive data access + processing  |
         |                          |                                   |
         | Active                   | Active data access + processing   |
         |                          |                                   |
         | Static key exfiltration  | One-time interaction              |
         |                          |                                   |
         | Dynamic key exfiltration | Ongoing interaction / code change |
         |                          |                                   |
         | Content exfiltration     | Ongoing, bulk interaction         |
         +--------------------------+-----------------------------------+

       Each of the attack types discussed in the previous section entails
       certain costs and risks.  These costs differ by attack and can be
       helpful in guiding response to pervasive attack.

       Depending on the attack, the attacker may be exposed to several types
       of risk, ranging from simply losing access to arrest or prosecution.
       In order for any of these negative consequences to occur, however,
       the attacker must first be discovered and identified.  So, the
       primary risk we focus on here is the risk of discovery and
       attribution.

       A passive pervasive attack is the simplest to mount in some ways.
       The base requirement is that the attacker obtain physical access to a
       communications medium and extract communications from it.  For
       example, the attacker might tap a fiber-optic cable, acquire a mirror
       port on a switch, or listen to a wireless signal.  The need for these
       taps to have physical access or proximity to a link exposes the
       attacker to the risk that the taps will be discovered.  For example,
       a fiber tap or mirror port might be discovered by network operators
       noticing increased attenuation in the fiber or a change in switch
       configuration.  Of course, passive pervasive attacks may be
       accomplished with the cooperation of the network operator, in which
       case there is a risk that the attacker's interactions with the
       network operator will be exposed.

       In many ways, the costs and risks for an active pervasive attack are
       similar to those for a passive pervasive attack, with a few
       additions.  An active attacker requires more robust network access
       than a passive attacker, since, for example, they will often need to
       transmit data as well as receive it.  In the wireless example above,

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       the attacker would need to act as a transmitter as well as a
       receiver, greatly increasing the probability the attacker will be
       discovered (e.g., using direction-finding technology).  Active
       attacks are also much more observable at higher layers of the
       network.  For example, an active attacker that attempts to use a mis-
       issued certificate could be detected via Certificate Transparency
       [RFC6962].

       In terms of raw implementation complexity, passive pervasive attacks
       require only enough processing to extract information from the
       network and store it.  Active pervasive attacks, by contrast, often
       depend on winning race conditions to inject packets into active
       connections.  So, active pervasive attacks in the core of the network
       require processing hardware that can operate at line speed (roughly
       100 Gbps to 1 Tbps in the core) to identify opportunities for attack
       and insert attack traffic in high-volume traffic.  Key exfiltration
       attacks rely on passive pervasive attack for access to encrypted
       data, with the collaborator providing keys to decrypt the data.  So,
       the attacker undertakes the cost and risk of a passive pervasive
       attack, as well as additional risk of discovery via the interactions
       that the attacker has with the collaborator.

       Some active attacks are more expensive than others.  For example,
       active man-in-the-middle (MITM) attacks require access to one or more
       points on a communication's network path that allow visibility of the
       entire session and the ability to modify or drop legitimate packets
       in favor of the attacker's packets.  A similar but weaker form of
       attack, called an active man-on-the-side (MOTS), requires access to
       only part of the session.  In an active MOTS attack, the attacker
       need only be able to inject or modify traffic on the network element
       the attacker has access to.  While this may not allow for full
       control of a communication session (as in an MITM attack), the
       attacker can perform a number of powerful attacks, including but not
       limited to: injecting packets that could terminate the session (e.g.,
       TCP RST packets), sending a fake DNS reply to redirect ensuing TCP
       connections to an address of the attacker's choice (i.e., winning a
       "DNS response race"), and mounting an HTTP redirect attack by
       observing a TCP/HTTP connection to a target address and injecting a
       TCP data packet containing an HTTP redirect.  For example, the system
       dubbed by researchers as China's "Great Cannon" [great-cannon] can
       operate in full MITM mode to accomplish very complex attacks that can
       modify content in transit, while the well-known Great Firewall of
       China is a MOTS system that focuses on blocking access to certain
       kinds of traffic and destinations via TCP RST packet injection.

       In this sense, static exfiltration has a lower risk profile than
       dynamic.  In the static case, the attacker need only interact with
       the collaborator a small number of times, possibly only once -- say,

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    RFC 7624              Confidentiality Threat Model           August 2015

       to exchange a private key.  In the dynamic case, the attacker must
       have continuing interactions with the collaborator.  As noted above,
       these interactions may be real, such as in-person meetings, or
       virtual, such as software modifications that render keys available to
       the attacker.  Both of these types of interactions introduce a risk
       that they will be discovered, e.g., by employees of the collaborator
       organization noticing suspicious meetings or suspicious code changes.

       Content exfiltration has a similar risk profile to dynamic key
       exfiltration.  In a content exfiltration attack, the attacker saves
       the cost and risk of conducting a passive pervasive attack.  The risk
       of discovery through interactions with the collaborator, however, is
       still present, and may be higher.  The content of a communication is
       obviously larger than the key used to encrypt it, often by several
       orders of magnitude.  So, in the content exfiltration case, the
       interactions between the collaborator and the attacker need to be
       much higher bandwidth than in the key exfiltration cases, with a
       corresponding increase in the risk that this high-bandwidth channel
       will be discovered.

       It should also be noted that in these latter three exfiltration
       cases, the collaborator also undertakes a risk that his collaboration
       with the attacker will be discovered.  Thus, the attacker may have to
       incur additional cost in order to convince the collaborator to
       participate in the attack.  Likewise, the scope of these attacks is
       limited to cases where the attacker can convince a collaborator to
       participate.  If the attacker is a national government, for example,
       it may be able to compel participation within its borders, but have a
       much more difficult time recruiting foreign collaborators.

       As noted above, the collaborator in an exfiltration attack can be
       unwitting; the attacker can steal keys or data to enable the attack.
       In some ways, the risks of this approach are similar to the case of
       an active collaborator.  In the static case, the attacker needs to
       steal information from the collaborator once; in the dynamic case,
       the attacker needs continued presence inside the collaborators'
       systems.  The main difference is that the risk in this case is of
       automated discovery (e.g., by intrusion detection systems) rather
       than discovery by humans.

    6.  Security Considerations

       This document describes a threat model for pervasive surveillance
       attacks.  Mitigations are to be given in a future document.

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    7.  References

    7.1.  Normative References

       [RFC6973]  Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,
                  Morris, J., Hansen, M., and R. Smith, "Privacy
                  Considerations for Internet Protocols", RFC 6973,
                  DOI 10.17487/RFC6973, July 2013,
                  <http://www.rfc-editor.org/info/rfc6973>.

    7.2.  Informative References

       [dec1]     Perlroth, N., Larson, J., and S. Shane, "N.S.A. Able to
                  Foil Basic Safeguards of Privacy on Web", The New York
                  Times, September 2013,
                  <http://www.nytimes.com/2013/09/06/us/
                  nsa-foils-much-internet-encryption.html>.

       [dec2]     The Guardian, "Project Bullrun -- classification guide to
                  the NSA's decryption program", September 2013,
                  <http://www.theguardian.com/world/interactive/2013/sep/05/
                  nsa-project-bullrun-classification-guide>.

       [dec3]     Ball, J., Borger, J., and G. Greenwald, "Revealed: how US
                  and UK spy agencies defeat internet privacy and security",
                  The Guardian, September 2013,
                  <http://www.theguardian.com/world/2013/sep/05/
                  nsa-gchq-encryption-codes-security>.

       [dir1]     Greenwald, G., "NSA collecting phone records of millions
                  of Verizon customers daily", The Guardian, June 2013,
                  <http://www.theguardian.com/world/2013/jun/06/
                  nsa-phone-records-verizon-court-order>.

       [dir2]     Greenwald, G. and E. MacAskill, "NSA Prism program taps in
                  to user data of Apple, Google and others", The Guardian,
                  June 2013, <http://www.theguardian.com/world/2013/jun/06/
                  us-tech-giants-nsa-data>.

       [dir3]     The Guardian, "Sigint -- how the NSA collaborates with
                  technology companies", September 2013,
                  <http://www.theguardian.com/world/interactive/2013/sep/05/
                  sigint-nsa-collaborates-technology-companies>.

       [DPRIVE]   Bortzmeyer, S., "DNS privacy considerations", Work in
                  Progress, draft-ietf-dprive-problem-statement-06, June
                  2015.

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    RFC 7624              Confidentiality Threat Model           August 2015

       [great-cannon]
                  Marczak, B., Weaver, N., Dalek, J., Ensafi, R., Fifield,
                  D., McKune, S., Rey, A., Scott-Railton, J., Deibert, R.,
                  and V. Paxson, "China's Great Cannon", The Citizen Lab,
                  University of Toronto, 2015,
                  <https://citizenlab.org/2015/04/chinas-great-cannon/>.

       [pass1]    Greenwald, G. and S. Ackerman, "How the NSA is still
                  harvesting your online data", The Guardian, June 2013,
                  <http://www.theguardian.com/world/2013/jun/27/
                  nsa-online-metadata-collection>.

       [pass2]    Ball, J., "NSA's Prism surveillance program: how it works
                  and what it can do", The Guardian, June 2013,
                  <http://www.theguardian.com/world/2013/jun/08/
                  nsa-prism-server-collection-facebook-google>.

       [pass3]    Greenwald, G., "XKeyscore: NSA tool collects 'nearly
                  everything a user does on the internet'", The Guardian,
                  July 2013, <http://www.theguardian.com/world/2013/jul/31/
                  nsa-top-secret-program-online-data>.

       [pass4]    MacAskill, E., Borger, J., Hopkins, N., Davies, N., and J.
                  Ball, "How does GCHQ's internet surveillance work?", The
                  Guardian, June 2013,
                  <http://www.theguardian.com/uk/2013/jun/21/
                  how-does-gchq-internet-surveillance-work>.

       [RFC1035]  Mockapetris, P., "Domain names - implementation and
                  specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,
                  November 1987, <http://www.rfc-editor.org/info/rfc1035>.

       [RFC1918]  Rekhter, Y., Moskowitz, B., Karrenberg, D., de Groot, G.,
                  and E. Lear, "Address Allocation for Private Internets",
                  BCP 5, RFC 1918, DOI 10.17487/RFC1918, February 1996,
                  <http://www.rfc-editor.org/info/rfc1918>.

       [RFC1939]  Myers, J. and M. Rose, "Post Office Protocol - Version 3",
                  STD 53, RFC 1939, DOI 10.17487/RFC1939, May 1996,
                  <http://www.rfc-editor.org/info/rfc1939>.

       [RFC3261]  Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston,
                  A., Peterson, J., Sparks, R., Handley, M., and E.
                  Schooler, "SIP: Session Initiation Protocol", RFC 3261,
                  DOI 10.17487/RFC3261, June 2002,
                  <http://www.rfc-editor.org/info/rfc3261>.

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    RFC 7624              Confidentiality Threat Model           August 2015

       [RFC3365]  Schiller, J., "Strong Security Requirements for Internet
                  Engineering Task Force Standard Protocols", BCP 61,
                  RFC 3365, DOI 10.17487/RFC3365, August 2002,
                  <http://www.rfc-editor.org/info/rfc3365>.

       [RFC3501]  Crispin, M., "INTERNET MESSAGE ACCESS PROTOCOL - VERSION
                  4rev1", RFC 3501, DOI 10.17487/RFC3501, March 2003,
                  <http://www.rfc-editor.org/info/rfc3501>.

       [RFC4033]  Arends, R., Austein, R., Larson, M., Massey, D., and S.
                  Rose, "DNS Security Introduction and Requirements",
                  RFC 4033, DOI 10.17487/RFC4033, March 2005,
                  <http://www.rfc-editor.org/info/rfc4033>.

       [RFC4303]  Kent, S., "IP Encapsulating Security Payload (ESP)",
                  RFC 4303, DOI 10.17487/RFC4303, December 2005,
                  <http://www.rfc-editor.org/info/rfc4303>.

       [RFC4949]  Shirey, R., "Internet Security Glossary, Version 2",
                  FYI 36, RFC 4949, DOI 10.17487/RFC4949, August 2007,
                  <http://www.rfc-editor.org/info/rfc4949>.

       [RFC5246]  Dierks, T. and E. Rescorla, "The Transport Layer Security
                  (TLS) Protocol Version 1.2", RFC 5246,
                  DOI 10.17487/RFC5246, August 2008,
                  <http://www.rfc-editor.org/info/rfc5246>.

       [RFC5321]  Klensin, J., "Simple Mail Transfer Protocol", RFC 5321,
                  DOI 10.17487/RFC5321, October 2008,
                  <http://www.rfc-editor.org/info/rfc5321>.

       [RFC6962]  Laurie, B., Langley, A., and E. Kasper, "Certificate
                  Transparency", RFC 6962, DOI 10.17487/RFC6962, June 2013,
                  <http://www.rfc-editor.org/info/rfc6962>.

       [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
                  "Specification of the IP Flow Information Export (IPFIX)
                  Protocol for the Exchange of Flow Information", STD 77,
                  RFC 7011, DOI 10.17487/RFC7011, September 2013,
                  <http://www.rfc-editor.org/info/rfc7011>.

       [RFC7258]  Farrell, S. and H. Tschofenig, "Pervasive Monitoring Is an
                  Attack", BCP 188, RFC 7258, DOI 10.17487/RFC7258, May
                  2014, <http://www.rfc-editor.org/info/rfc7258>.

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    RFC 7624              Confidentiality Threat Model           August 2015

       [spiegel1] Appelbaum, J., Horchert, J., Reissmann, O., Rosenbach, M.,
                  Schindler, J., and C. Stocker, "NSA's Secret Toolbox: Unit
                  Offers Spy Gadgets for Every Need", Spiegel Online,
                  December 2013, <http://www.spiegel.de/international/world/
                  nsa-secret-toolbox-ant-unit-offers-spy-gadgets-for-every-
                  need-a-941006.html>.

       [spiegel2] Appelbaum, J., Gibson, A., Guarnieri, C., Muller-Maguhn,
                  A., Poitras, L., Rosenbach, M., Schmundt, H., and M.
                  Sontheimer, "The Digital Arms Race: NSA Preps America for
                  Future Battle", Spiegel Online, January 2015,
                  <http://www.spiegel.de/international/world/new-snowden-
                  docs-indicate-scope-of-nsa-preparations-for-cyber-battle-
                  a-1013409.html>.

       [TOR1]     Schneier, B., "How the NSA Attacks Tor/Firefox Users With
                  QUANTUM and FOXACID", Schneier on Security, October 2013,
                  <https://www.schneier.com/blog/archives/2013/10/
                  how_the_nsa_att.html>.

       [TOR2]     The Guardian, "'Tor Stinks' presentation -- read the full
                  document", October 2013,
                  <http://www.theguardian.com/world/interactive/2013/oct/04/
                  tor-stinks-nsa-presentation-document>.

    IAB Members at the Time of Approval

       Jari Arkko (IETF Chair)
       Mary Barnes
       Marc Blanchet
       Ralph Droms
       Ted Hardie
       Joe Hildebrand
       Russ Housley
       Erik Nordmark
       Robert Sparks
       Andrew Sullivan
       Dave Thaler
       Brian Trammell
       Suzanne Woolf

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    Acknowledgements

       Thanks to Dave Thaler for the list of attacks and taxonomy; to
       Security Area Directors Stephen Farrell, Sean Turner, and Kathleen
       Moriarty for starting and managing the IETF's discussion on pervasive
       attack; and to Stephan Neuhaus, Mark Townsley, Chris Inacio,
       Evangelos Halepilidis, Bjoern Hoehrmann, Aziz Mohaisen, Russ Housley,
       Joe Hall, Andrew Sullivan, the IEEE 802 Privacy Executive Committee
       SG, and the IAB Privacy and Security Program for their input.

    Authors' Addresses

       Richard Barnes

       Email: rlb@ipv.sx

       Bruce Schneier

       Email: schneier@schneier.com

       Cullen Jennings

       Email: fluffy@cisco.com

       Ted Hardie

       Email: ted.ietf@gmail.com

       Brian Trammell

       Email: ietf@trammell.ch

       Christian Huitema

       Email: huitema@huitema.net

       Daniel Borkmann

       Email: dborkman@iogearbox.net

    Barnes, et al.                Informational                    [Page 24]