Network Working Group R. Barnes
Internet-Draft
Intended status: Informational B. Schneier
Expires: March 15, 2015
C. Jennings
T. Hardie
B. Trammell
C. Huitema
D. Borkmann
September 11, 2014
Confidentiality in the Face of Pervasive Surveillance: A Threat Model
and Problem Statement
draft-iab-privsec-confidentiality-threat-00
Abstract
Documents published in 2013 have revealed several classes of
"pervasive" attack on Internet communications. In this document we
develop a threat model that describes these pervasive attacks. We
start by assuming a completely passive adversary with an interest in
indiscriminate eavesdropping that can observe network traffic, then
expand the threat model with a set of verified attacks that have been
published. Based on this threat model, we discuss the techniques
that can be employed in Internet protocol design to increase the
protocols robustness to pervasive attacks.
Status of This Memo
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This Internet-Draft will expire on March 15, 2015.
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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, 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 passive adversary, 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 adversary. 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. Section 6 provides some high-
level guidance on how Internet protocols can defend against the
threats described here.
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2. Terminology
This document makes extensive use of standard security and privacy
terminology; see [RFC4949] and [RFC6973]. In addition, we use a few
terms that are specific to the attacks discussed here:
Pervasive Attack: An attack on Internet protocols 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.
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
protocol, but who provides information about that interaction
(keys or data) to an attacker.
Key Exfiltration: The transmission of keying material for an
encrypted communication from a collaborator to an attacker
Content Exfiltration: The transmission of the content of a
communication from a collaborator to an attacker
Unwitting Collaborator: A collaborator that provides information to
the attacker not deliberately, but because the attacker has
exploited some technology used by the collaborator.
3. An Idealized Pervasive Passive Adversary
We assume a pervasive passive adversary, an indiscriminate
eavesdropper on an Internet-attached computer network that
o can observe every packet of all communications at any or every hop
in any network path between an initiator and a recipient; and
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o can observe data at rest in intermediate systems between the
endpoints controlled by the initiator and recipient; but
o takes no other action with respect to these communications (i.e.,
blocking, modification, injection, etc.).
This adversary is less capable than those which we know to have
compromised the Internet from press reports, elaborated in Section 4,
but represents the threat to communications privacy by a single
entity interested in remaining undetectable.
The techniques available to our ideal adversary 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 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, assuming
uncompromised encryption implementations and key material, but
provides less complete protection against inference, especially
inference based only on unprotected portions of communications (e.g.
IP and TCP headers for TLS).
3.1. Information subject to direct observation
Protocols which do not encrypt their payload make the entire content
of the communication available to a PPA 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 all DNS queries and answers generated
by the activities of any protocol are available to a the adversary.
Protocols which imply the storage of some data at rest in
intermediaries leave this data subject to observation by an adversary
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.
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3.2. Information useful for inference
Inference is information extracted from later analysis of an observed
communication, and/or correlation of observed information with
information available from other sources. Indeed, most useful
inference performed by a our ideal adversary 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 [RFC5246]) still expose
all the information in their network and transport layer headers to a
PPA, 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
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 privascy 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 from the protocol design side
to limit such correlation by a PPA, and that the existence of such
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data sources in many cases greatly complicates the problem of
protecting privacy by hardening protocols alone.
3.3. An illustration of an ideal passive attack
To illustrate how capable even this limited adversary is, we explore
the non-anonymity of even encrypted IP traffic by examining in detail
some inference techniques for associating a set of addresses with an
individual, in order to illustrate the difficulty of defending
communications against a PPA. 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.
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
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numerous than users, a PPA 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.
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 adversary 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 [RFC5321]
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>
"
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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 PPA that sees at least one
copy of the message.
An idealized adversary 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 adversary can still receive a copy of
public mailing lists like "perpass".
3.3.5. Tracking address usage with web cookies
Many web sites only encrypt a small fraction of their transactions.
A popular pattern was 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, a PPA 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 adversary 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.
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4. Reported Instances of Large-Scale Attacks
The situation in reality is more bleak than that suggested by an
analysis of our idealized adversary. 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 adversary, 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 adversary, 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).
o Use of implants (covert modifications or malware) to undermine
security and anonymity features [dec2][TOR1][TOR2]. For example:
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* 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.
* 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" 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 attacks. Because of the resources required to
achieve pervasive scale, pervasive 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.
5. Threat Model
Given these disclosures, we must consider broader threat model.
Pervasive surveillance aims to collect information across a large
number of Internet communications, observing 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
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the bulk of Internet traffic and to the keying material required to
decrypt any traffic that has been encrypted (though 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 all Internet
communications.
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 attackers, who can simply listen in on
communications as they transit the network, and "active attackers",
who can modify or delete packets in addition to simply collecting
them.
In the context of pervasive attack, 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 attacks. In the pervasive attack case, however,
both passive and active attacks are undertaken closer to the core of
the network, greatly expanding the scope and capability of the
attacker.
A passive attacker with access to a large portion of the Internet can
analyze collected traffic to create a much more detailed view of user
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behavior than an attacker that collects at a single point. Even the
usual claim that encryption defeats passive attackers is weakened,
since a pervasive passive 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.
The reports on the NSA XKEYSCORE system would make it an example of
such an attacker.
A pervasive active attacker likewise has capabilities beyond those of
a localized active attacker. Active 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 active attacker with multiple accesses at core points of
the Internet is able to overcome these topological limitations and
apply attacks over a much broader scope. Being positioned in the
core of the network rather than the edge can also enable a pervasive
active attacker to reroute targeted traffic. Pervasive active
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 active attack are also often in a
position to subvert authentication, the traditional response to
active attack. 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 for pervasive attack 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
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These attacks all rely on a "collaborator" endpoint providing the
attacker with some information, either keys or data. These attacks
have not traditionally been considered in security analyses of
protocols, since they happen outside of 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
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 was stored unencrypted. In
these cases, the operator of the server would be a collaborator
(wittingly or unwittingly). By contrast, in the NSA MUSCULAR
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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 |
+--------------------------+-----------------------------------+
In order to realize an attack of each of the types discussed above,
the attacker has to incur certain costs and undertake certain 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 happen, 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 attack is the simplest attack 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 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.
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In many ways, the costs and risks for an active attack are similar to
those for a passive 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
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). 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 attacks require
only enough processing to extract information from the network and
store it. Active attacks, by contrast, often depend on winning race
conditions to inject pakets into active connections. So active
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 passive 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
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
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 passive 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
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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. Responding to Pervasive Attack
Given this threat model, how should the Internet technical community
respond to pervasive attack?
The cost and risk considerations discussed above can provide a guide
to response. Namely, responses to passive attack should close off
avenues for attack that are safe, scalable, and cheap, forcing the
attacker to mount attacks that expose it to higher cost and risk.
In this section, we discuss a collection of high-level approaches to
mitigating pervasive attacks. These approaches are not meant to be
exhaustive, but rather to provide general guidance to protocol
designers in creating protocols that are resistant to pervasive
attack.
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+--------------------------+----------------------------------------+
| Attack Class | High-level mitigations |
+--------------------------+----------------------------------------+
| Passive observation | Encryption for confidentiality |
| | |
| Passive inference | ??? |
| | |
| Active | Authentication, monitoring |
| | |
| Static key exfiltration | Encryption with per-session state |
| | (PFS) |
| | |
| Dynamic key exfiltration | Transparency, validation of end |
| | systems |
| | |
| Content exfiltration | Object encryption, distributed systems |
+--------------------------+----------------------------------------+
The traditional mitigation to passive attack is to render content
unintelligible to the attacker by applying encryption, for example,
by using TLS or IPsec [RFC5246][RFC4301]. Even without
authentication, encryption will prevent a passive attacker from being
able to read the encrypted content. Exploiting unauthenticated
encryption requires an active attack (man in the middle); with
authentication, a key exfiltration attack is required.
The additional capabilities of a pervasive passive attacker, however,
require some changes in how protocol designers evaluate what
information is encrypted. In addition to directly collecting
unencrypted data, a pervasive passive attacker can also make
inferences about the content of encrypted messages based on what is
observable. For example, if a user typically visits a particular set
of web sites, then a pervasive passive attacker observing all of the
user's behavior can track the user based on the hosts the user
communicates with, even if the user changes IP addresses, and even if
all of the connections are encrypted.
Thus, in designing protocols to be resistant to pervasive passive
attacks, protocol designers should consider what information is left
unencrypted in the protocol, and how that information might be
correlated with other traffic. Information that cannot be encrypted
should be anonymized, i.e., it should be dissociated from other
information. For example, the Tor overlay routing network anonymizes
IP addresses by using multi-hop onion routing [TOR].
As with traditional, limited active attacks, the basic mitigation to
pervasive active attack is to enable the endpoints of a communication
to authenticate each other. However, as noted above, attackers that
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can mount pervasive active attacks can often subvert the authorities
on which authentication systems rely. Thus, in order to make
authentication systems more resilient to pervasive attack, it is
beneficial to monitor these authorities to detect misbehavior that
could enable active attack. For example, DANE and Certificate
Transparency both provide mechanisms for detecting when a CA has
issued a certificate for a domain name without the authorization of
the holder of that domain name [RFC6962][RFC6698].
While encryption and authentication protect the security of
individual sessions, these sessions may still leak information, such
as IP addresses or server names, that a pervasive attacker can use to
correlate sessions and derive additional information about the
target. Thus, pervasive attack highlights the need for anonymization
technologies, which make correlation more difficult. Typical
approaches to anonymization against traffic analysis include:
o Aggregation: Routing sessions for many endpoints through a common
mid-point (e.g., an HTTP proxy). Since the midpoint appears as
the end of the communication, individual endpoints cannot be
distinguished.
o Onion routing: Routing a session through several mid-points,
rather than directly end-to-end, with encryption that guarantees
that each node can only see the previous and next hops [TOR].
This ensures that the source and destination of a communication
are never revealed simultaneously.
o Multi-path: Routing different sessions via different paths (even
if they originate from the same endpoint). This reduces the
probability that the same attacker will be able to collect many
sessions.
An encrypted, authenticated session is safe from content-monitoring
attacks in which neither end collaborates with the attacker, but can
still be subverted by the endpoints. The most common ciphersuites
used for HTTPS today, for example, are based on using RSA encryption
in such a way that if an attacker has the private key, the attacker
can derive the session keys from passive observation of a session.
These ciphersuites are thus vulnerable to a static key exfiltration
attack - if the attacker obtains the server's private key once, then
they can decrypt all past and future sessions for that server.
Static key exfiltration attacks are prevented by including ephemeral,
per-session secret information in the keys used for a session. Most
IETF security protocols include modes of operation that have this
property. These modes are known in the literature under the heading
"perfect forward secrecy" (PFS) because even if an adversary has all
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of the secrets for one session, the next session will use new,
different secrets and the attacker will not be able to decrypt it.
The Internet Key Exchange (IKE) protocol used by IPsec supports PFS
by default [RFC4306], and TLS supports PFS via the use of specific
ciphersuites [RFC5246].
Dynamic key exfiltration cannot be prevent by protocol means. By
definition, any secrets that are used in the protocol will be
transmitted to the attacker and used to decrypt what the protocol
encrypts. Likewise, no technical means will stop a willing
collaborator from sharing keys with an attacker. However, this
attack model also covers "unwitting collaborators", whose technical
resources are collaborating with the attacker without their owners'
knowledge. This could happen, for example, if flaws are built into
products or if malware is injected later on.
The best defense against becoming an unwitting collaborator is thus
to assure that end systems are well-vetted and secure. Transparency
is a major tool in this process [secure]. Open source software is
easier to evaluate for potential flaws than proprietary software, by
a wider array of independent analysts. Products that conform to
standards for cryptography and security protocols are limited in the
ways they can misbehave. And standards processes that are open and
transparent help ensure that the standards themselves do not provide
avenues for attack.
Standards can also define protocols that provide greater or lesser
opportunity for dynamic key exfiltration. Collaborators engaging in
key exfiltration through a standard protocol will need to use covert
channels in the protocol to leak information that can be used by the
attacker to recover the key. Such use of covert channels has been
demonstrated for SSL, TLS, and SSH [key-recovery]. Any protocol bits
that can be freely set by the collaborator can be used as a covert
channel, including, for example, TCP options or unencrypted traffic
sent before a STARTTLS message in SMTP or XMPP. Protocol designers
should consider what covert channels their protocols expose, and how
those channels can be exploited to exfiltrate key information.
Content exfiltration has some similarity to the dynamic exfiltration
case, in that nothing can prevent a collaborator from revealing what
they know, and the mitigations against becoming an unwitting
collaborator apply. In this case, however, applications can limit
what the collaborator is able to reveal. For example, the S/MIME and
PGP systems for secure email both deny intermediate servers access to
certain parts of the message [RFC5750][RFC2015]. Even if a server
were to provide an attacker with full access, the attacker would
still not be able to read the protected parts of the message.
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Mechanisms like S/MIME and PGP are often referred to as "end-to-end"
security mechanisms, as opposed to "hop-by-hop" or "end-to-middle"
mechanisms like the use of SMTP over TLS. These two different
mechanisms address different types of attackers: Hop-by-hop
mechanisms protect from attackers on the wire (passive or active),
while end-to-end mechansims protect against attackers within
intermediate nodes. Thus, neither of these mechanisms provides
complete protection by itself. For example:
o Two users messaging via Facebook over HTTPS are protected against
passive and active attackers in the network between the users and
Facebook. However, if Facebook is a collaborator in an
exfiltration attack, their communications can still be monitored.
They would need to encrypt their messages end-to-end in order to
protect themselves against this risk.
o Two users exchanging PGP-protected email have protected the
content of their exchange from network attackers and intermediate
servers, but the header information (e.g., To and From addresses)
is unnecessarily exposed to passive and active attackers that can
see communications among the mail agents handling the email
messages. These mail agents need to use hop-by-hop encryption and
traffic analysis mitigation to address this risk.
Mechanisms such as S/MIME and PGP are also known as "object-based"
security mechanisms (as opposed to "communications security"
mechanisms), since they operate at the level of objects, rather than
communications sessions. Such secure object can be safely handled by
intermediaries in order to realize, for example, store and forward
messaging. In the examples above, the encrypted instant messages or
email messages would be the secure objects.
The mitigations to the content exfiltration case are thus to regard
participants in the protocol as potential passive attackers
themselves, and apply the mitigations discussed above with regard to
passive attack. Information that is not necessary for these
participants to fulfill their role in the protocol can be encrypted,
and other information can be anonymized.
In summary, many of the basic tools for mitigating pervasive attack
already exist. As Edward Snowden put it, "properly implemented
strong crypto systems are one of the few things you can rely on"
[snowden]. The task for the Internet community is to ensure that
applications are able to use the strong crypto systems we have
defined - for example, TLS with PFS ciphersuites - and that these
properly implemented. (And, one might add, turned on!) Some of this
work will require architectural changes to applications, e.g., in
order to limit the information that is exposed to servers. In many
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other cases, however, the need is simply to make the best use we can
of the cryptographic tools we have.
7. Acknowledgements
o Thaler for list of attacks and taxonomy
o Security ADs for starting and managing the perpass discussion
o See PPA acks as well
8. TODO
o Ensure all bases are covered WRT threats to confidentiality
o Consider moving mitigations to a separate document per program
description
o Look at better alignment with draft-farrell-perpass-attack
o Better coverage of traffic analysis - PPA helped somewhat here but
the problem is hard
o Terminology alignment (after the program agrees the structure is
good)
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>.
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[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>.
[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>.
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[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/>.
[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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