Confidentiality in the Face of Pervasive Surveillance: A Threat Model and Problem Statement
draft-iab-privsec-confidentiality-threat-03
The information below is for an old version of the document.
| Document | Type |
This is an older version of an Internet-Draft that was ultimately published as RFC 7624.
|
|
|---|---|---|---|
| Authors | Richard Barnes , Bruce Schneier , Cullen Fluffy Jennings , Ted Hardie , Brian Trammell , Christian Huitema , Daniel Borkmann | ||
| Last updated | 2015-02-20 | ||
| RFC stream | Internet Architecture Board (IAB) | ||
| Formats | |||
| Additional resources | |||
| Stream | IAB state | (None) | |
| Consensus boilerplate | Unknown | ||
| IAB shepherd | (None) |
draft-iab-privsec-confidentiality-threat-03
Network Working Group R. Barnes
Internet-Draft
Intended status: Informational B. Schneier
Expires: August 24, 2015
C. Jennings
T. Hardie
B. Trammell
C. Huitema
D. Borkmann
February 20, 2015
Confidentiality in the Face of Pervasive Surveillance: A Threat Model
and Problem Statement
draft-iab-privsec-confidentiality-threat-03
Abstract
Documents published since initial revelations in 2013 have revealed
several classes of pervasive surveillance attack on Internet
communications. In this document we develop a threat model that
describes these pervasive attacks. We start by assuming an attacker
with an interest in undetected, indiscriminate eavesdropping, then
expand the threat model with a set of verified attacks that have been
published.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
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Drafts is at http://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on August 24, 2015.
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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
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include Simplified BSD License text as described in Section 4.e of
the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
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, both in terms of the
amount of Internet communications targeted, and in terms of 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 flow access 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 which 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.
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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 here:
Flow Access Attack: An eavesdropping attack in which the packets
in a traffic stream between two endpoints are eavesdropped upon,
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. Flow access attacks are
undetectable from the endpoints.
Flow Modification Attack: An attack which includes both
eavesdropping (as in a flow access attack) as well as
modification, addition, or removal of packets in a traffic stream,
or modification of treatment of packets in the traffic stream.
Flow modification attacks provide more capabilities to the
attacker at the cost of possible detection at the endpoints.
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]
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 extracted 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, but who deliberately provides information about
that interaction to an attacker.
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Unwitting Collaborator: An entity that is a legitimate participant
in a communication, and who is the source of information obtained
by the attacker without the entity's consent or intention, because
the attacker has exploited some technology used by the entity.
Key Exfiltration: The transmission of 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 Pervasive Flow Access Attacker
In considering the threat posed by pervasive surveillance, we begin
by defining an idealized pervasive flow access attacker. While this
attacker is less capable than those which 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 on an
Internet-attached computer network that:
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 - e.g., URLs identifying
content or email addresses identifying individuals from application-
layer headers. Inference, on the other hand, involves analyzing
eavesdropped information to derive new information from it; e.g.,
searching for application or behavioral fingerprints in observed
traffic to derive information about the observed individual from
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them, in absence of directly-observed sources of the same
information. The use of encryption to protect confidentiality is
generally enough to prevent direct observation of unencrypted
content, assuming uncompromised encryption implementations and key
material. However, it provides less complete protection against
inference, especially inference based only on unprotected portions of
communications (e.g. IP and TCP headers for TLS [RFC5246]).
3.1. Information subject to direct observation
Protocols which 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 which encrypts 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
DPRIVE working group, all DNS queries and answers generated by the
activities of any protocol are available to the attacker.
Protocols which imply the storage of some data at rest in
intermediaries (e.g. SMTP [RFC5321]) 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 which 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 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
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cryptographic protocols themselves, e.g. the TLS session identifier,
may leak information that can be used for correlation and inference.
While this information is much less semantically rich than the
application payload, it can still be useful for the 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 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 which 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 flow access 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 which have no obvious connection with
personal data can be correlated with other information which can
paint a very rich behavioral picture, that only takes one unprotected
link in the chain to associate with an identity.
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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
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 IPFIX [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
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provides an incentive for the attacker to secure the cooperation of
the ISP in order to automate this correlation.
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 clear-text 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 clear text, 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
pre-determined 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. websites, 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 websites,
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 MAC Addresses
Moving back down the stack, technologies like Ethernet or Wi-Fi use
MAC Addresses to identify link-level destinations. MAC Addresses
assigned according to IEEE-802 standards are unique to the device.
If the link is publicly accessible, an attacker can track it. For
example, the attacker can track the wireless traffic at public Wi-Fi
networks. Simple devices can monitor the traffic, and reveal which
MAC Addresses are present. 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 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.
Given that large-scale databases of the MAC addresses of wireless
access points for geolocation purposes have been known to exist for
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some time, the attacker could easily build a database linking MAC
Addresses and device or user identities, and use it to track the
movement of devices and of their owners.
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.
First, they have confirmed that these agencies have capabilities in
line with those of our idealized attacker, thorugh the large-scale
passive collection of Internet traffic [pass1][pass2][pass3][pass4].
For example: - The NSA 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. - The GCHQ
Tempora system appears to have access to around 1,500 major cables
passing through the UK. - The NSA MUSCULAR program tapped cables
between data centers belonging to major service providers. - 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 go beyond those available to our
idealized attacker, including:
o Decryption of TLS-protected Internet sessions [dec1][dec2][dec3].
For example, the NSA BULLRUN project appears to have had a budget
of around $250M per year to undermine encryption through multiple
approaches.
o Insertion of NSA devices as a man-in-the-middle of Internet
transactions [TOR1][TOR2]. For example, the NSA QUANTUM system
appears to use several different techniques to hijack HTTP
connections, ranging from DNS response injection to HTTP 302
redirects.
o Direct acquisition of bulk data and metadata from service
providers [dir1][dir2][dir3]. For example, the NSA PRISM program
provides the agency with access to many types of user data (e.g.,
email, chat, VoIP).
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o Use of implants (covert modifications or malware) to undermine
security and anonymity features [dec2][TOR1][TOR2]. For example:
* NSA appears to use the QUANTUM man-in-the-middle system to
direct users to a FOXACID server, which delivers an implant to
compromise the browser of a user of the Tor anonymous
communications network.
* Implants are apparently available for Cisco, Juniper, Huawei,
Dell, and HP network elements, provided by the NSA Advanced
Network Technology group [spiegel1]
* Compromised hosts at botnet scale, using tools by the NSA's
Remote Operations Center [spiegel3]
* The BULLRUN program mentioned above includes the addition of
covert modifications to software as one means to undermine
encryption.
* There is also some suspicion that NSA modifications to the
DUAL_EC_DRBG random number generator were made to ensure that
keys generated using that generator could be predicted by NSA.
These suspicions have been reinforced by reports that RSA
Security was paid roughly $10M to make DUAL_EC_DRBG the default
in their products.
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.
It's important to note that although the attacks mentioned above were
executed by 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 which have a
high degree of pervasiveness with regard to the Internet in China.
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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. his 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.
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: flow access attackers, who can simply listen in on
communications as they transit the network, and flow modification
attackers, who can modify or delete packets in addition to simply
collecting them.
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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 flow modification attacks. However, as we now
know, both passive and flow modification 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 flow access 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 flow access attackers is weakened,
since a pervasive flow access attacker can infer relationships from
correlations over large numbers of sessions, e.g., 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.
A pervasive flow modification attacker likewise has capabilities
beyond those of a localized flow modification 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 a pervasive
flow modification attacker to reroute targeted traffic, amplifying
the ability to perform both eavesdropping and traffic injection.
Pervasive flow modification attackers can also benefit from pervasive
passive collection to identify vulnerable hosts.
While not directly related to pervasiveness, attackers that are in a
position to mount a pervasive flow modification 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
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"weakest link"). Subversion of authorities in this way can allow an
flow modification 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.
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, typically of a long-lived key. 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
psuedorandom 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 risk is to data at rest as opposed to 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
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keys or content rather than the party providing them willingly. In
these cases, the party may not be aware that they are collaborating,
at least at a human level. 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 NSA to collect
unencrypted user data.
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,
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the attacker must first be discovered and identified. So the primary
risk we focus on here is the risk of discovery and attribution.
A flow access 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, flow access 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 flow modification attack are
similar to those for a flow access attack, with a few additions. An
flow modification attacker requires more robust network access than a
flow access attacker, since for example they will often need to
transmit data as well as receiving it. In the wireless example
above, the attacker would need to act as an transmitter as well as
receiver, greatly increasing the probability the attacker will be
discovered (e.g., using direction-finding technology). flow
modification attacks are also much more observable at higher layers
of the network. For example, an flow modification attacker that
attempts to use a mis-issued certificate could be detected via
Certificate Transparency [RFC6962].
In terms of raw implementation complexity, flow access attacks
require only enough processing to extract information from the
network and store it. flow modification attacks, by contrast, often
depend on winning race conditions to inject pakets into active
connections. So flow modification attacks in the core of the network
require processing hardware to that can operate at line speed
(roughly 100Gbps to 1Tbps in the core) to identify opportunities for
attack and insert attack traffic in a high-volume traffic. Key
exfiltration attacks rely on flow access 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 flow access
attack, as well as additional risk of discovery via the interactions
that the attacker has with the collaborator.
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 to
exchange a private key. In the dynamic case, the attacker must have
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continuing interactions with the collaborator. As noted above these
interactions may 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 flow access 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 case 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 to 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. IANA Considerations
This document has no actions for IANA.
8. 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, as well as
the IAB Privacy and Security Program, for their input.
9. References
9.1. 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, July
2013.
9.2. Informative References
[pass1] The Guardian, "How the NSA is still harvesting your online
data", 2013,
<http://www.theguardian.com/world/2013/jun/27/
nsa-online-metadata-collection>.
[pass2] The Guardian, "NSA's Prism surveillance program: how it
works and what it can do", 2013,
<http://www.theguardian.com/world/2013/jun/08/
nsa-prism-server-collection-facebook-google>.
[pass3] The Guardian, "XKeyscore: NSA tool collects 'nearly
everything a user does on the internet'", 2013,
<http://www.theguardian.com/world/2013/jul/31/
nsa-top-secret-program-online-data>.
[pass4] The Guardian, "How does GCHQ's internet surveillance
work?", n.d., <http://www.theguardian.com/uk/2013/jun/21/
how-does-gchq-internet-surveillance-work>.
[dec1] The New York Times, "N.S.A. Able to Foil Basic Safeguards
of Privacy on Web", 2013,
<http://www.nytimes.com/2013/09/06/us/
nsa-foils-much-internet-encryption.html>.
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[dec2] The Guardian, "Project Bullrun - classification guide to
the NSA's decryption program", 2013,
<http://www.theguardian.com/world/interactive/2013/sep/05/
nsa-project-bullrun-classification-guide>.
[dec3] The Guardian, "Revealed: how US and UK spy agencies defeat
internet privacy and security", 2013,
<http://www.theguardian.com/world/2013/sep/05/
nsa-gchq-encryption-codes-security>.
[TOR] The Tor Project, "Tor", 2013,
<https://www.torproject.org/>.
[TOR1] Schneier, B., "How the NSA Attacks Tor/Firefox Users With
QUANTUM and FOXACID", 2013,
<https://www.schneier.com/blog/archives/2013/10/
how_the_nsa_att.html>.
[TOR2] The Guardian, "'Tor Stinks' presentation - read the full
document", 2013,
<http://www.theguardian.com/world/interactive/2013/oct/04/
tor-stinks-nsa-presentation-document>.
[dir1] The Guardian, "NSA collecting phone records of millions of
Verizon customers daily", 2013,
<http://www.theguardian.com/world/2013/jun/06/
nsa-phone-records-verizon-court-order>.
[dir2] The Guardian, "NSA Prism program taps in to user data of
Apple, Google and others", 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", 2013,
<http://www.theguardian.com/world/interactive/2013/sep/05/
sigint-nsa-collaborates-technology-companies>.
[secure] Schneier, B., "NSA surveillance: A guide to staying
secure", 2013,
<http://www.theguardian.com/world/2013/sep/05/
nsa-how-to-remain-secure-surveillance>.
[snowden] Technology Review, "NSA Leak Leaves Crypto-Math Intact but
Highlights Known Workarounds", 2013,
<http://www.technologyreview.com/news/519171/nsa-leak-
leaves-crypto-math-intact-but-highlights-known-
workarounds/>.
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[spiegel1]
C Stocker, ., "NSA's Secret Toolbox: Unit Offers Spy
Gadgets for Every Need", December 2013,
<http://www.spiegel.de/international/world/nsa-secret-
toolbox-ant-unit-offers-spy-gadgets-for-every-need-
a-941006.html>.
[spiegel3]
H Schmundt, ., "The Digital Arms Race: NSA Preps America
for Future Battle", January 2014,
<http://www.spiegel.de/international/world/new-snowden-
docs-indicate-scope-of-nsa-preparations-for-cyber-battle-
a-1013409.html>.
[key-recovery]
Golle, P., "The Design and Implementation of Protocol-
Based Hidden Key Recovery", 2003,
<http://crypto.stanford.edu/~pgolle/papers/escrow.pdf>.
[RFC1035] Mockapetris, P., "Domain names - implementation and
specification", STD 13, RFC 1035, November 1987.
[RFC1918] Rekhter, Y., Moskowitz, R., Karrenberg, D., Groot, G., and
E. Lear, "Address Allocation for Private Internets", BCP
5, RFC 1918, February 1996.
[RFC1939] Myers, J. and M. Rose, "Post Office Protocol - Version 3",
STD 53, RFC 1939, May 1996.
[RFC2015] Elkins, M., "MIME Security with Pretty Good Privacy
(PGP)", RFC 2015, October 1996.
[RFC2821] Klensin, J., "Simple Mail Transfer Protocol", RFC 2821,
April 2001.
[RFC3261] Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston,
A., Peterson, J., Sparks, R., Handley, M., and E.
Schooler, "SIP: Session Initiation Protocol", RFC 3261,
June 2002.
[RFC3365] Schiller, J., "Strong Security Requirements for Internet
Engineering Task Force Standard Protocols", BCP 61, RFC
3365, August 2002.
[RFC3501] Crispin, M., "INTERNET MESSAGE ACCESS PROTOCOL - VERSION
4rev1", RFC 3501, March 2003.
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[RFC3851] Ramsdell, B., "Secure/Multipurpose Internet Mail
Extensions (S/MIME) Version 3.1 Message Specification",
RFC 3851, July 2004.
[RFC4033] Arends, R., Austein, R., Larson, M., Massey, D., and S.
Rose, "DNS Security Introduction and Requirements", RFC
4033, March 2005.
[RFC4301] Kent, S. and K. Seo, "Security Architecture for the
Internet Protocol", RFC 4301, December 2005.
[RFC4303] Kent, S., "IP Encapsulating Security Payload (ESP)", RFC
4303, December 2005.
[RFC4306] Kaufman, C., "Internet Key Exchange (IKEv2) Protocol", RFC
4306, December 2005.
[RFC4949] Shirey, R., "Internet Security Glossary, Version 2", RFC
4949, August 2007.
[RFC5246] Dierks, T. and E. Rescorla, "The Transport Layer Security
(TLS) Protocol Version 1.2", RFC 5246, August 2008.
[RFC5321] Klensin, J., "Simple Mail Transfer Protocol", RFC 5321,
October 2008.
[RFC5655] Trammell, B., Boschi, E., Mark, L., Zseby, T., and A.
Wagner, "Specification of the IP Flow Information Export
(IPFIX) File Format", RFC 5655, October 2009.
[RFC5750] Ramsdell, B. and S. Turner, "Secure/Multipurpose Internet
Mail Extensions (S/MIME) Version 3.2 Certificate
Handling", RFC 5750, January 2010.
[RFC6120] Saint-Andre, P., "Extensible Messaging and Presence
Protocol (XMPP): Core", RFC 6120, March 2011.
[RFC6962] Laurie, B., Langley, A., and E. Kasper, "Certificate
Transparency", RFC 6962, June 2013.
[RFC6698] Hoffman, P. and J. Schlyter, "The DNS-Based Authentication
of Named Entities (DANE) Transport Layer Security (TLS)
Protocol: TLSA", RFC 6698, August 2012.
[RFC7011] Claise, B., Trammell, B., and P. Aitken, "Specification of
the IP Flow Information Export (IPFIX) Protocol for the
Exchange of Flow Information", STD 77, RFC 7011, September
2013.
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[RFC7258] Farrell, S. and H. Tschofenig, "Pervasive Monitoring Is an
Attack", BCP 188, RFC 7258, May 2014.
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@redhat.com
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