Commit cea2439c authored by Frits Vaandrager's avatar Frits Vaandrager
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We have combined model learning with abstraction techniques to infer models of the OpenSSH, Bitvise and DropBear SSH server implementations. We have also
formalized several security and functional properties drawn from the SSH RFC specifications. We have verified these
properties on the learned models using model checking and have uncovered several minor inconsistencies, though crucially, the security critical properties were met
by all implementation.
by all implementations.
Abstraction was provided by a {\dmapper} component placed between the
{\dlearner} and the {\dsut}. The {\dmapper} was constructed from an
......@@ -17,17 +17,18 @@ explore most aforementioned behavior.
There were several challenges encountered. Firstly, building a {\dmapper} presented a considerable technical challenge, as it required re-structuring of an actual
SSH implementation. Secondly, because we used classical learning algorithms, we had to ensure that the abstracted implementation behaved
like a deterministic Mealy Machine. Herein, time induced non-determinism was difficult to eliminate. Buffering also presented problems,
like a deterministic Mealy Machine. Here time-induced non-determinism was difficult to eliminate. Buffering also presented problems,
leading to a considerable increase in the number of states. Moreover, the systems analyzed were relatively slow, which meant learning took
several days\marginpar{\tiny Erik: For a single server, right??}. This was compounded by the size of the learning alphabet, and it forced us into using a reduced alphabet for two of the analyzed implementations.
Limitations of the work, hence elements for future work, are several. First of all, the {\dmapper} was not formalized, unlike in~\cite{TCP2016}, thus we did not
Limitations of the work, hence possibilities for future work, are several. First of all, the {\dmapper} was not formalized, unlike in~\cite{TCP2016}, thus we did not
produce a concretization of the abstract models. Consequently, model checking results cannot be fully transferred to the actual implementations. Formal definition
of the mapper and concretization of the learned models would tackle this. The {\dmapper} also caused considerable redundancy in the learned models, re-tweaking the abstractions used, in particular those for managing channels, could alleviate this problem while also improving learning times. This in turn would facilitate learning using expanded alphabets instead of resorting to restricted alphabets.
Furthermore, the {\dmapper} abstraction could be refined, so more insight into the implementation is gained. In particular, parameters
Furthermore, the {\dmapper} abstraction could be refined, to give more
insight into the implementations. In particular, parameters,
such as the session identifier or data sent over channels, could be extracted from the {\dmapper} and potentially handled by existing Register Automata learners\cite{ralib2015,tomte2015}. These learners
can infer systems with parameterized alphabets, state variables and simple operations on data. Finally, we had to eliminate any timing related behavior, as it could not be handled by the classical learners used. To that end, preliminary work on learning timed automata\cite{GrinchteinJL10} could be leveraged .
can infer systems with parameterized alphabets, state variables and simple operations on data. Finally, we had to eliminate any timing-related behavior, as it could not be handled by the classical learners used. To that end, preliminary work on learning timed automata\cite{GrinchteinJL10} could be leveraged.
Despite these limitations, our work provides a compelling application of learning and model checking in a security setting, on a widely used protocol. We hope this lays
some more groundwork for further case studies, as well as advances learning techniques.
some more groundwork for further case studies, as well as advances in learning techniques.
\section{Introduction}\label{introduction}
The SSH protocol is used to interact securely with remote machines. Alongside TLS and IPSec, SSH is among the most frequently used network security protocols~\cite{Albrecht2009Plaintext}. Due to its significant user base and sensitive nature, flaws in the protocol or its implementation could have major impact. It therefore comes as no surprise that SSH has attracted scrutiny from the security community.
SSH has been subjected to various security analyses \cite{Albrecht2009Plaintext,Bellare2004Breaking,Williams2011Analysis,Paterson2010PlaintextDependent}.
Formal methods have been applied in analysing implementations of
SSH: Protocol state machines of SSH's transport layer
were used in a manual code review of OpenSSH
\cite{Poll_rigorous_2011} and a formal program verification of a Java
implementation of SSH \cite{PollSchubert07}. These models have also
been used for model based testing \cite{Boss2012}.
Udrea et al.~\cite{Udrea_rule-based_2008} use static analysis to check
two implementations of SSH against an extensive set of rules.
SSH is a security protocols that is widely to interact securely with
remote machines. SSH -- or more precisely, the transport layer
protocol of SSH -- has been subjected to security analysis
\cite{Williams2011Analysis}, incl.\ analyses that revealed
cryptographic shortcomings
\cite{Albrecht2009Plaintext,Bellare2004Breaking,Paterson2010PlaintextDependent}.
%Academic researchers have so far focused more on the theoretical aspects than on implementations of the protocol.
But whereas these analyses consider the abstract cryptographic
protocol, this paper looks at actual implementations of SSH, and
investigates flaws in the program logic of these implementations,
rather than cryptographic flaws. Such logical flaws have occurred in
implementations of other security protocols, notably TLS, with Apple's
'goto fail' bug and the FREAK attack \cite{Beurdouche:2017}. For this
we use model learning (a.k.a.\ active automata learning)
\cite{Angluin1987Learning,Vaa17} to infer state machines of three SSH
implementations, which we then analyze by model checking for
conformance to both functional and security properties.
In this work, we use model learning (a.k.a.\ active automata learning) \cite{Angluin1987Learning,Vaa17},
to infer state machines of three SSH implementations, which we then verify by model checking.
Model learning has previously been applied to infer state machines of EMV bank cards~\cite{Aarts2013Formal}, electronic passports~\cite{Aarts2010Inference}, hand-held readers for online banking~\cite{Chalupar2014Automated}, and implementations of TCP~\cite{TCP2016} and TLS~\cite{RuiterProtocol}. Model learning aims to obtain a state machine model of a black-box system by providing inputs and observing outputs. The learned state model corresponds to the observed behavior, and can be used in system analysis. Since model learning builds from a finite number of observations, we can never be sure that the learned model is correct. To that end, advanced conformance algorithms are employed \cite{LeeY96}, which yield some confidence that the system inferred is in fact correct. In the context of testing protocols, model learning can be seen as a form of protocol state fuzzing, whereby unexpected inputs are sent to a system under test in the hope of exposing hidden anomalies. In model learning, inputs are sent with no regard to the order imposed by the protocol. Any anomalies are then exposed in the learned model.
The properties we verify for the inferred state machines are based on
the RFCs that specify SSH \cite{rfc4251,rfc4252,rfc4253,rfc4254}.
These properties are formalized in LTL and verified using
NuSMV~\cite{NuSMV2}. Manually verifying these properties would be
difficult, as the learned models are complex. Moreover, formalizing
properties means we can also better assess and overcome vagueness or
under-specification in the RFC standards.
Having obtained models, we use model checking to automatically verify their conformance to both functional and security properties. The properties are drawn out of the RFC specifications \cite{rfc4251,rfc4252,rfc4253,rfc4254} and formalized in LTL. They are then checked for truth on the learned model using NuSMV~\cite{NuSMV2}. Manually verifying these properties would be difficult, as the learned models are reasonably large. Moreover, formalizing properties means we can also better assess and overcome vagueness or under-specification in the RFC standards.
Model learning obtains a state machine model of a black-box system by
providing inputs and observing outputs. Since model learning uses a
finite number of observations, we can never be sure that the learned
model is correct. To that end, advanced conformance algorithms are employed
\cite{LeeY96}, which yield some confidence that the system inferred is
in fact complete. In the context of testing security protocols, model
learning can be seen as a form of fuzzing, where inputs are sent in
unexpected orders to expose any hidden anomalies.
%Moreover, the very satisfaction of properties can be used to refine the learned model.
%In cases where the learned model does not satisfy a security property, the proving sequence of inputs can either signal non-conformance of the system, or can be used to further refine the learned model.
\paragraph{Related work}
Our work is borne out of 2 recent theses \cite{Verleg2016,Toon2016} and to our knowledge, is the first combined application of model learning and model checking in verifying SSH implementations.
Previous applications of formal verification methods involve model based testing\cite{Boss2012} or static analysis\cite{Poll_rigorous_2011,Udrea_rule-based_2008}. The former limited analysis to the Transport layer. The latter required access to source code, which might not be available for proprietary implementations. \cite{Udrea_rule-based_2008} formulated an extensive set of rules, which were then used by a static analysis tool to verify C implementations. The set of rules covered all layers and it described both elements of message ordering and data flow, whereas we only analyze message ordering. However, rules were tied to routines in the code, so had to be slightly adapted to fit the different implementations. By contrast, our properties are defined at an abstract level so don't need such tailoring. Moreover employing a black box approach makes our approach easily portable and means we don't impose any constraints on the actual implementation of the system (like for example, that it is implemented in C).
%The SSH protocol has been investigated for cryptographic flaws, e.g.\
%\cite{Williams2011Analysis,Albrecht2009Plaintext,Bellare2004Breaking,Paterson2010PlaintextDependent},
%but that research does not consider logical flaws in actual software
%implementations.
Our approach, most closely resembles work on TCP~\cite{TCP2016}, where they also combine classical learning and model checking to analyze various TCP implementations, with our work being more focused on security properties. Other case studies involving model learning rely on manual analysis of the learned models~\cite{Aarts2013Formal,RuiterProtocol,deRuiter16OpenSSL}, though in~\cite{Chalupar2014Automated} a limited part of the analysis was also done using model checking. Our work differs, since we use a model checker to automatically check specifications. Inference of protocols has also been done from observing network traffic~\cite{Wang2011Inferring}. Such inference is limited to the traces that occur over a network. Inference is further hampered if the analyzed protocols communicate over encrypted channels, as this severely limits
information available from traces without knowledge of the security key.
%Whereas the combined work of \cite{Poll_rigurous2011}, \cite{Boss2012} leverages model
%based testing on a specification model, we
Udrea et al.\cite{Udrea_rule-based_2008} also investigated SSH
implementations for logical flaws. They used a static analysis tool to
check two C implementations of SSH against an extensive set of rules.
These rules not only express properties of the SSH protocol logic, but
also of message formats and support for earlier versions and various
options. Our analysis only considers the protocol logic. However,
their rules were tied to routines in the code, so had to be slightly adapted
to fit the different implementations. In contrast, our properties are
defined at an abstract level so do not need such tailoring. Moreover,
our black box approach approach means we can analyze any implementation
of SSH, not just C implementations.
%Moreover, the model checker is also used as means of testing the learned model.
Formal models of SSH in the form of state machines have been used
before, namely for a manual code review of OpenSSH
\cite{Poll_rigorous_2011}, formal program verification of a Java
implementation of SSH \cite{PollSchubert07}, and for model based
testing of SSH implementations \cite{Boss2012}. All this research
only considered the SSH transport layer, and not the other SSH
protocol layers.
This paper is borne out of 2 recent theses \cite{Verleg2016,Toon2016}
and is to our knowledge the first combined application of model
learning and model checking in verifying SSH implementations, or more
generally, implementations of any network security protocol.
Model learning has previously been applied to infer state machines of
EMV bank cards~\cite{Aarts2013Formal}, electronic
passports~\cite{Aarts2010Inference}, hand-held readers for online
banking~\cite{Chalupar2014Automated}, and implementations of
TCP~\cite{TCP2016} and TLS~\cite{RuiterProtocol}. Some of these case
studies relied on manual analysis of the learned models
\cite{Aarts2013Formal,Aarts2010Inference,RuiterProtocol}, but some
also used model checkers \cite{TCP2016,Chalupar2014Automated}.
Instead of using active learning as we do, it is also possible to use
passive learning to obtain protocol state machines
\cite{Wang2011Inferring}. Here network traffic is observed, and not
actively generated. This can then also provide a probabilistic
characterization of normal network traffic, but it cannot uncover
implementation flaws that occur in strange message flows, which is our
goal.
%Our approach, most closely resembles work on TCP~\cite{TCP2016},
%where they also combine classical learning and model checking to
%analyze various TCP implementations, with our work being more focused
%on security properties. Other case studies involving model learning
%rely on manual analysis of the learned models
%~\cite{Aarts2013Formal,TCP2016,Chalupar2014Automated,RuiterProtocol}.
%Our work differs, since we use a model checker to automatically check
%specifications. Inference of protocols has also been done from
%observing network traffic ~\cite{Wang2011Inferring}. Such inference
%is limited to the traces that occur over a network. Inference is
%further hampered if the analyzed protocols communicate over encrypted
%channels, as this severely limits information available from traces without knowledge of the security key.
%Model checkers provide for an automatic way of verifying properties.
%Manual verification would be difficult manually, as the state machines are large.
%State machines inferred are large and hard to check manually for properties. Moreover, the very satisfaction of these properties by the learned model can be used to generate tests. Model checkers provide for an automated way to check conformance of learned models.
%
%In this thesis, we will be using protocol state fuzzing to extract an implementations state machine. All fuzzing algorithms rely on the idea of sending unexpected input data to a system under test (SUT) in the hope that this triggers anomalies. Using protocol state fuzzing, however, we will fuzz on the \textit{order} of otherwise correctly-formed messages.
%This technique has previously been applied to infer state machines of EMV bank cards~\cite{Aarts2013Formal}, electronic passports~\cite{Aarts2010Inference} and hand-held readers for online banking~\cite{Chalupar2014Automated}. Furthermore, implementations of TCP~\cite{Janssen2015Learning} and TLS~\cite{RuiterProtocol} have state-fuzzed. As in these works, we implement state fuzzing
%
......
......@@ -12,10 +12,10 @@ Certain arrangements had to be made including the setting of timing parameters t
\begin{figure*}
\centering
\includegraphics[scale=0.30]{ssh-server}
\caption{OpenSSH server. States are collected in 3 clusters,
\caption{Model of the OpenSSH server. {\normalfont States are collected in 3 clusters,
indicated by the rectangles, where each cluster corresponds to
one of the protocol layers.
We eliminate redundant states and information induced by the {\dmapper}, as well as states present in successful rekey sequences. Wherever rekey was permitted, we replaced the rekey states and transitions by a single \textsl{REKEY SEQUENCE} transition. We also factor out edges common to states within a cluster. We replace common disconnecting edges, by one edge from the cluster to the disconnect state. Common self loop edges are colored, and the actual i/o information only appears on one edge. Transitions with similar start and end states are joined together on the same edge. Transition labels are kept short by regular expressions(UA\_* stands for all inputs starting with UA\_) or by factoring out common start strings. Green edges highlight the happy flow. }
We eliminate redundant states and information induced by the {\dmapper}, as well as states present in successful rekey sequences. Wherever rekey was permitted, we replaced the rekey states and transitions by a single \textsl{REKEY SEQUENCE} transition. We also factor out edges common to states within a cluster. We replace common disconnecting edges, by one edge from the cluster to the disconnect state. Common self loop edges are colored, and the actual i/o information only appears on one edge. Transitions with similar start and end states are joined together on the same edge. Transition labels are kept short by regular expressions(UA\_* stands for all inputs starting with UA\_) or by factoring out common start strings. Green edges highlight the happy flow. }}
\label{fig:sshserver}
\end{figure*}
......
......@@ -44,8 +44,9 @@ the messages \textsc{service\_accept}, \textsc{ua\_accept},
Connection layer we only use messages for channel management and the
terminal functionality. Finally, because we will only explore
protocol behaviour after SSH versions have been exchanged, we exclude
these messages for exchaning version numbers. \marginpar{\tiny Erik: I
rephrased all this to make it simpler. Is it still ok?}
these messages for exchanging version numbers.
%\marginpar{\tiny Erik: I
%rephrased all this to make it simpler. Is it still ok?}
The resulting lists of inputs for the three protocol layers are given
in tables~\ref{trans-alphabet}-\ref{conn-alphabet}. In some
......@@ -169,105 +170,101 @@ keys. Receipt of the \textsc{newkeys} response from the {\dsut} will
make the {\dmapper} use the new keys earlier negotiated in place of
the older ones, if such existed.
The {\dmapper} contains two other
state variables,
one is a buffer for storing open channels. It is initially empty and
is increased/decreased on \textsc{ch\_open} and \textsc{ch\_close}
inputs respectively. The other is initially set to 0, and stores the
sequence number of the last received message. This is then used when
constructing \textsc{unimpl} inputs. \marginpar{\tiny Erik: I don't get this
bit}
The {\dmapper} contains a buffer for storing channels opened, which is initially empty.
On a \textsc{ch\_open} from the learner, the {\dmapper} adds a channel to the buffer
with a randomly generated channel identifier, on a \textsc{ch\_close}, it removes the channel
(if there was any). The buffer size, or the maximum number of opened channels, is limited to one. Initially,
the buffer is empty.
Lastly, the {\dmapper} also stores the sequence number of the last received message from the {\dsut}.
This number is then used when constructing \textsc{unimpl} inputs.
In the following cases, inputs are answered by the {\dmapper} directly
instead of being sent to the {\dsut} fo find out its response:
instead of being sent to the {\dsut} to find out its response:
\begin{enumerate}
\item on receiving a \textsc{ch\_open} input and the buffer has reached the size limit, the {\dmapper} directly responds with \textsc{ch\_max};
\item on receiving any input operating on a channel (all Connection layer inputs other than \textsc{ch\_open}) when the buffer is empty, the
{\dmapper} directly responds with \textsc{ch\_none};
\item if connection with the {\dsut} was terminated, the {\dmapper}
responds with a \textsc{no\_conn} message, as sending furtheer
responds with a \textsc{no\_conn} message, as sending further
messages to the {\dsut} is pointless in that case;
\item if no channel has been opened (the buffer variable is empty) or the maximum number of channels was reached (in our experiments 1), cases which prompt the {\dmapper}
to respond with \textsc{ch\_none}, or \textsc{ch\_max} respectively
\marginpar{\tiny Erik: i don't get this 2nd bullet; something is
missing?}
\end{enumerate}
Overall, we notice that in many ways, the {\dmapper} acts similarly to an SSH client. Hence it is unsurprising that it was built off an existing
implementation.
\subsection{Compacting SSH into a small Mealy machine}
\subsection{Practical complications}
There are three practical complications in learning models of SSH
servers: (1) an SSH server may exhibit \emph{non-determistic} behaviour; (2)
a single input to the server can produce a \emph{sequence} of outputs
ratheer than just a single output, and (3) \emph{buffering} behaviour of the
server. These complication are discussed below.
%There are three practical complications in learning models of SSH
%servers: (1) an SSH server may exhibit \emph{non-determistic}
%behaviour; (2) a single input to the server can produce a
%\emph{sequence} of outputs ratheer than just a single output, and (3)
%\emph{buffering} behaviour of the server. These complication are
%discussed below.
SSH implementations can exhibit non-determistic behaviour. The
learning algorithm cannot deal with non-determinism, so the {\dmapper}
not only provides abstraction, but it also ensures\marginpar{\tiny
Erik:'checks' instead of 'ensures'??} that the abstract representation
shown to the learner behaves a deterministic Mealy machine.
There are a few sources of non-determinism:
learning algorithm cannot cope with non-determinism -- learning will
not terminate -- so this has to be detected, which our {\dmapper} does.
There are a few sources of non-determinism in SSH:
\begin{enumerate}
\item Underspecification in the SSH specification (for example, by not
specifying the order of certain messages) allows
non-deterministic behaviour of implementations. Even if client
specifying the order of certain messages) allows some
non-deterministic behaviour. And even if client
and server do implement a fixed order for messages they sent, the
asynchronous nature of comunication between them means the
interleaving of messages sent and received may vary. Moreover,
asynchronous nature of communication means that the
interleaving of sent and received messages may vary. Moreover,
client and server are free to intersperse \textsc{debug} and
\textsc{ignore} messages at any given time; the \textsc{ignore}
messages are aimed to thwart traffic analysis.
\textsc{ignore} messages at any given time\footnote{The \textsc{ignore}
messages are aimed to thwart traffic analysis.}
\item Timing is another source of non-deterministic behaviour. For
example, the {\dmapper} might time-out before the {\dsut} had
sent its response.
In our experiments we tried to set time-out periods so that this
did not occur. However, other timing-related quirks can still
cause non-deterministism. For example, {\dsuts} behave
cause non-deterministism. For example, some {\dsuts} behave
unexpectedly when a new query is received too shortly after the
previous one.
%For example, a trace in which a valid user authentication is performed within five milliseconds after an authentication request on DropBear can cause the authentication to (wrongly) fail.
\end{enumerate}
%
To detect non-determinism, we cache all new observations in an SQLite database and verify observations against this cache.
\marginpar{\tiny Erik: the text does not explain what we do if non-determinism
is detected. Do we ignore the new trace and report the old one from the
database? And/or warn that non-determinism is happening?}
The cache also enables us to answer to queries answered before
without running inputs on the {\dsut}. This transcended consecutive
learning runs, as we could reload the cache from a previous run,
enabling quick response of all queries up to the point the previous
run ended.\marginpar{\tiny Erik: but the trace still has to be
re-run, right, if we want to extend it beyond the end of the previous
run?}
To detect non-determinism, the {\dmapper} caches all observations
in an SQLite database and verifies if a new observation is different
to one cached from a previous protocol run. If so, it raises
a warning, which then needs to be manually investigated.
An added benefit of this cache is that is allows the {\dmapper} to
supply answer to some inputs without actually sending them to the
{\dsut}. This sped up learning a lot when we had to restart
experiments: any new experiment on the same {\dsut} could start where
the previous experiment left of, without re-running all inputs. This
was an important benefit, as experiments could take several days.
%A subsequent identical learning run can quickly resume from where the previous one was ended, as the cache from the previous run is used to quickly respond to all queries up to the point the previous run ended.
An SSH server may also produce a sequence of outputs in response to an
input. This means it is not behaving as a Mealy machines, which allow
allow for only one output. Dealing with this is simple: the {\dmapper}
concatenates all outputs into one, and it produces this sequence
as the single output to the {\dlearner}.
Another challenge is presented by buffering, which leads to an
explosion of states in learning, as buffers cannot be described
succinctly by Mealy machines.
We have encountered buffering in two situations. Firstly, some
implementations buffer responses during a key re-exchange; only once
the rekeying is complete, are all these queued responses released, all
at once. This leads to a \textsc{newkeys} response (indicating
rekeying has completed), directly followed by all buffered responses.
This would lead to non-termination of the learning algorithm, as for
every sequence of buffered messages there is a different respone. To
prevent this, we treat the sequence of queued responses
as a single output \textsc{buffered}.
Secondly, buffering happens when opening and closing channels,
since a SUT can close only as many channels as have previously been
opened. For this reason, we restrict the number of simultaneously open
Another practical problem beside non-determinism is that an SSH server
may produce a sequence of outputs in response to a single input. This
means it is not behaving as a Mealy machines, which allows for
only one output. Dealing with this is simple: the {\dmapper}
concatenates all outputs into one, and it produces this sequence as
the single output to the {\dlearner}.
A final challenge is presented by forms of `buffering', which we
encountered in two situations. Firstly, some implementations buffer
incoming requests during a key re-exchange; only once the rekeying is
complete are all these messages processed. This leads to a
\textsc{newkeys} response (indicating rekeying has completed),
directly followed by all the responses to the buffered requests. This
would lead to non-termination of the learning algorithm, as for every
sequence of buffered messages the response is different. To
prevent this, we treat the sequence of queued responses as a single
output \textsc{buffered}.
Secondly, buffering happens when opening and closing channels, since a
{\dsut} can close only as many channels as have previously been opened.
Learning this behavior would lead to an infinite state machine, as we
would need a state `there are $n$ channels open' for every number $n$.
For this reason, we restrict the number of simultaneously open
channels to one. The {\dmapper} returns a custom response
\textsc{ch\_max} to a \textsc{ch\_open} message whenever this limit is
reached.
......@@ -94,9 +94,9 @@ tabsize=2
\begin{abstract}
We apply model learning on three SSH implementations to infer state machine models, which
are then verified by a model checker for functional and security properties. Our results show that
all tested SSH servers satisfy the security properties, but only
all tested SSH servers satisfy the security properties, but
satisfy the functional properties
only by a varying degree. Moreover, the state machines of the
only to a varying degree. Moreover, the state machines of the
implementations differ significantly, allowing them to be
effectively fingerprinted.
......
......@@ -590,9 +590,22 @@ machine learning algorithms},
title = {Model Learning},
volume = {60},
number = {2},
month = feb,
year = {2017},
pages = {86--95},
publisher = {ACM},
address = {New York, NY, USA},
}
@article{Beurdouche:2017,
author = {Beurdouche, B. and Bhargavan, K. and Delignat-Lavaud, A. and Fournet, C. and Kohlweiss, M. and Pironti, A. and Strub, P.-Y. and Zinzindohoue, J. K.},
title = {A Messy State of the Union: Taming the Composite State Machines of TLS},
journal = {Communications of the ACM},
volume = {60},
number = {2},
year = {2017},
pages = {99--107},
publisher = {ACM},
address = {New York, NY, USA},
}
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