814 research outputs found
Detecting time-fragmented cache attacks against AES using Performance Monitoring Counters
Cache timing attacks use shared caches in multi-core processors as side
channels to extract information from victim processes. These attacks are
particularly dangerous in cloud infrastructures, in which the deployed
countermeasures cause collateral effects in terms of performance loss and
increase in energy consumption. We propose to monitor the victim process using
an independent monitoring (detector) process, that continuously measures
selected Performance Monitoring Counters (PMC) to detect the presence of an
attack. Ad-hoc countermeasures can be applied only when such a risky situation
arises. In our case, the victim process is the AES encryption algorithm and the
attack is performed by means of random encryption requests. We demonstrate that
PMCs are a feasible tool to detect the attack and that sampling PMCs at high
frequencies is worse than sampling at lower frequencies in terms of detection
capabilities, particularly when the attack is fragmented in time to try to be
hidden from detection
Software Grand Exposure: SGX Cache Attacks Are Practical
Side-channel information leakage is a known limitation of SGX. Researchers
have demonstrated that secret-dependent information can be extracted from
enclave execution through page-fault access patterns. Consequently, various
recent research efforts are actively seeking countermeasures to SGX
side-channel attacks. It is widely assumed that SGX may be vulnerable to other
side channels, such as cache access pattern monitoring, as well. However, prior
to our work, the practicality and the extent of such information leakage was
not studied.
In this paper we demonstrate that cache-based attacks are indeed a serious
threat to the confidentiality of SGX-protected programs. Our goal was to design
an attack that is hard to mitigate using known defenses, and therefore we mount
our attack without interrupting enclave execution. This approach has major
technical challenges, since the existing cache monitoring techniques experience
significant noise if the victim process is not interrupted. We designed and
implemented novel attack techniques to reduce this noise by leveraging the
capabilities of the privileged adversary. Our attacks are able to recover
confidential information from SGX enclaves, which we illustrate in two example
cases: extraction of an entire RSA-2048 key during RSA decryption, and
detection of specific human genome sequences during genomic indexing. We show
that our attacks are more effective than previous cache attacks and harder to
mitigate than previous SGX side-channel attacks
PerfWeb: How to Violate Web Privacy with Hardware Performance Events
The browser history reveals highly sensitive information about users, such as
financial status, health conditions, or political views. Private browsing modes
and anonymity networks are consequently important tools to preserve the privacy
not only of regular users but in particular of whistleblowers and dissidents.
Yet, in this work we show how a malicious application can infer opened websites
from Google Chrome in Incognito mode and from Tor Browser by exploiting
hardware performance events (HPEs). In particular, we analyze the browsers'
microarchitectural footprint with the help of advanced Machine Learning
techniques: k-th Nearest Neighbors, Decision Trees, Support Vector Machines,
and in contrast to previous literature also Convolutional Neural Networks. We
profile 40 different websites, 30 of the top Alexa sites and 10 whistleblowing
portals, on two machines featuring an Intel and an ARM processor. By monitoring
retired instructions, cache accesses, and bus cycles for at most 5 seconds, we
manage to classify the selected websites with a success rate of up to 86.3%.
The results show that hardware performance events can clearly undermine the
privacy of web users. We therefore propose mitigation strategies that impede
our attacks and still allow legitimate use of HPEs
DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization
Recent research has demonstrated that Intel's SGX is vulnerable to various
software-based side-channel attacks. In particular, attacks that monitor CPU
caches shared between the victim enclave and untrusted software enable accurate
leakage of secret enclave data. Known defenses assume developer assistance,
require hardware changes, impose high overhead, or prevent only some of the
known attacks. In this paper we propose data location randomization as a novel
defensive approach to address the threat of side-channel attacks. Our main goal
is to break the link between the cache observations by the privileged adversary
and the actual data accesses by the victim. We design and implement a
compiler-based tool called DR.SGX that instruments enclave code such that data
locations are permuted at the granularity of cache lines. We realize the
permutation with the CPU's cryptographic hardware-acceleration units providing
secure randomization. To prevent correlation of repeated memory accesses we
continuously re-randomize all enclave data during execution. Our solution
effectively protects many (but not all) enclaves from cache attacks and
provides a complementary enclave hardening technique that is especially useful
against unpredictable information leakage
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