Confidentiality in the Face of Pervasive Surveillance: A Threat Model and Problem Statement
RFC 7624
| Document | Type | RFC - Informational (August 2015) | |
|---|---|---|---|
| Authors | Richard Barnes , Bruce Schneier , Cullen Fluffy Jennings , Ted Hardie , Brian Trammell , Christian Huitema , Daniel Borkmann | ||
| Last updated | 2015-08-20 | ||
| RFC stream | Internet Architecture Board (IAB) | ||
| Formats | |||
| Stream | IAB state | Published RFC | |
| Consensus boilerplate | Unknown | ||
| IAB shepherd | (None) |
RFC 7624
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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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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[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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[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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[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]