2,507 research outputs found
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
Stacco: Differentially Analyzing Side-Channel Traces for Detecting SSL/TLS Vulnerabilities in Secure Enclaves
Intel Software Guard Extension (SGX) offers software applications enclave to
protect their confidentiality and integrity from malicious operating systems.
The SSL/TLS protocol, which is the de facto standard for protecting
transport-layer network communications, has been broadly deployed for a secure
communication channel. However, in this paper, we show that the marriage
between SGX and SSL may not be smooth sailing.
Particularly, we consider a category of side-channel attacks against SSL/TLS
implementations in secure enclaves, which we call the control-flow inference
attacks. In these attacks, the malicious operating system kernel may perform a
powerful man-in-the-kernel attack to collect execution traces of the enclave
programs at page, cacheline, or branch level, while positioning itself in the
middle of the two communicating parties. At the center of our work is a
differential analysis framework, dubbed Stacco, to dynamically analyze the
SSL/TLS implementations and detect vulnerabilities that can be exploited as
decryption oracles. Surprisingly, we found exploitable vulnerabilities in the
latest versions of all the SSL/TLS libraries we have examined.
To validate the detected vulnerabilities, we developed a man-in-the-kernel
adversary to demonstrate Bleichenbacher attacks against the latest OpenSSL
library running in the SGX enclave (with the help of Graphene) and completely
broke the PreMasterSecret encrypted by a 4096-bit RSA public key with only
57286 queries. We also conducted CBC padding oracle attacks against the latest
GnuTLS running in Graphene-SGX and an open-source SGX-implementation of mbedTLS
(i.e., mbedTLS-SGX) that runs directly inside the enclave, and showed that it
only needs 48388 and 25717 queries, respectively, to break one block of AES
ciphertext. Empirical evaluation suggests these man-in-the-kernel attacks can
be completed within 1 or 2 hours.Comment: CCS 17, October 30-November 3, 2017, Dallas, TX, US
Ozone: Efficient Execution with Zero Timing Leakage for Modern Microarchitectures
Time variation during program execution can leak sensitive information. Time
variations due to program control flow and hardware resource contention have
been used to steal encryption keys in cipher implementations such as AES and
RSA. A number of approaches to mitigate timing-based side-channel attacks have
been proposed including cache partitioning, control-flow obfuscation and
injecting timing noise into the outputs of code. While these techniques make
timing-based side-channel attacks more difficult, they do not eliminate the
risks. Prior techniques are either too specific or too expensive, and all leave
remnants of the original timing side channel for later attackers to attempt to
exploit.
In this work, we show that the state-of-the-art techniques in timing
side-channel protection, which limit timing leakage but do not eliminate it,
still have significant vulnerabilities to timing-based side-channel attacks. To
provide a means for total protection from timing-based side-channel attacks, we
develop Ozone, the first zero timing leakage execution resource for a modern
microarchitecture. Code in Ozone execute under a special hardware thread that
gains exclusive access to a single core's resources for a fixed (and limited)
number of cycles during which it cannot be interrupted. Memory access under
Ozone thread execution is limited to a fixed size uncached scratchpad memory,
and all Ozone threads begin execution with a known fixed microarchitectural
state. We evaluate Ozone using a number of security sensitive kernels that have
previously been targets of timing side-channel attacks, and show that Ozone
eliminates timing leakage with minimal performance overhead
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