arXiv subjects

Shweta Shinde

Publications and source records attributed to Shweta Shinde.

25 records · Page 2Linked to original sources

arXiv2019

BesFS: A POSIX Filesystem for Enclaves with a Mechanized Safety Proof

New trusted computing primitives such as Intel SGX have shown the feasibility of running user-level applications in enclaves on a commodity trusted processor without trusting a large OS. However, the OS can still compromise the integrity of an enclave by tampering with the system call return values. In fact, it has been shown that a subclass of these attacks, called Iago attacks, enables arbitrary logic execution in enclave programs. Existing enclave systems have very large TCB and they implement ad-hoc checks at the system call interface which are hard to verify for completeness. To this end, we present BesFS--the first filesystem interface which provably protects the enclave integrity against a completely malicious OS. We prove 167 lemmas and 2 key theorems in 4625 lines of Coq proof scripts, which directly proves the safety properties of the BesFS specification. BesFS comprises of 15 APIs with compositional safety and is expressive enough to support 31 real applications we test. BesFS integrates into existing SGX-enabled applications with minimal impact to TCB. BesFS can serve as a reference implementation for hand-coded API checks.

cs.CR↗

arXiv2019

Keystone: An Open Framework for Architecting TEEs

Trusted execution environments (TEEs) are being used in all the devices from embedded sensors to cloud servers and encompass a range of cost, power constraints, and security threat model choices. On the other hand, each of the current vendor-specific TEEs makes a fixed set of trade-offs with little room for customization. We present Keystone -- the first open-source framework for building customized TEEs. Keystone uses simple abstractions provided by the hardware such as memory isolation and a programmable layer underneath untrusted components (e.g., OS). We build reusable TEE core primitives from these abstractions while allowing platform-specific modifications and application features. We showcase how Keystone-based TEEs run on unmodified RISC-V hardware and demonstrate the strengths of our design in terms of security, TCB size, execution of a range of benchmarks, applications, kernels, and deployment models.

cs.CR↗

arXiv2019

Privado: Practical and Secure DNN Inference with Enclaves

Cloud providers are extending support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation as well as an increase in adoption. In this paper, we ask a timely question: "Can third-party cloud services use Intel SGX enclaves to provide practical, yet secure DNN Inference-as-a-service?" We first demonstrate that DNN models executing inside enclaves are vulnerable to access pattern based attacks. We show that by simply observing access patterns, an attacker can classify encrypted inputs with 97% and 71% attack accuracy for MNIST and CIFAR10 datasets on models trained to achieve 99% and 79% original accuracy respectively. This motivates the need for PRIVADO, a system we have designed for secure, easy-to-use, and performance efficient inference-as-a-service. PRIVADO is input-oblivious: it transforms any deep learning framework that is written in C/C++ to be free of input-dependent access patterns thus eliminating the leakage. PRIVADO is fully-automated and has a low TCB: with zero developer effort, given an ONNX description of a model, it generates compact and enclave-compatible code which can be deployed on an SGX cloud platform. PRIVADO incurs low performance overhead: we use PRIVADO with Torch framework and show its overhead to be 17.18% on average on 11 different contemporary neural networks.

cs.CR↗

arXiv2019

Quantitative Verification of Neural Networks And its Security Applications

Neural networks are increasingly employed in safety-critical domains. This has prompted interest in verifying or certifying logically encoded properties of neural networks. Prior work has largely focused on checking existential properties, wherein the goal is to check whether there exists any input that violates a given property of interest. However, neural network training is a stochastic process, and many questions arising in their analysis require probabilistic and quantitative reasoning, i.e., estimating how many inputs satisfy a given property. To this end, our paper proposes a novel and principled framework to quantitative verification of logical properties specified over neural networks. Our framework is the first to provide PAC-style soundness guarantees, in that its quantitative estimates are within a controllable and bounded error from the true count. We instantiate our algorithmic framework by building a prototype tool called NPAQ that enables checking rich properties over binarized neural networks. We show how emerging security analyses can utilize our framework in 3 concrete point applications: quantifying robustness to adversarial inputs, efficacy of trojan attacks, and fairness/bias of given neural networks.

cs.CR↗

arXiv2019

Practical Verifiable In-network Filtering for DDoS defense

In light of ever-increasing scale and sophistication of modern DDoS attacks, it is time to revisit in-network filtering or the idea of empowering DDoS victims to install in-network traffic filters in the upstream transit networks. Recent proposals show that filtering DDoS traffic at a handful of large transit networks can handle volumetric DDoS attacks effectively. However, the innetwork filtering primitive can also be misused. Transit networks can use the in-network filtering service as an excuse for any arbitrary packet drops made for their own benefit. For example, transit networks may intentionally execute filtering services poorly or unfairly to discriminate their competing neighbor ASes while claiming that they drop packets for the sake of DDoS defense. We argue that it is due to the lack of verifiable filtering - i.e., no one can check if a transit network executes the filter rules correctly as requested by the DDoS victims. To make in-network filtering a more robust defense primitive, in this paper, we propose a verifiable in-network filtering, called VIF, that exploits emerging hardware-based trusted execution environments (TEEs) and offers filtering verifiability to DDoS victims and neighbor ASes. Our proof of concept demonstrates that a VIF filter implementation on commodity servers with TEE support can handle traffic at line rate (e.g., 10 Gb/s) and execute up to 3,000 filter rules. We show that VIF can easily scale to handle larger traffic volume (e.g., 500 Gb/s) and more complex filtering operations (e.g., 150,000 filter rules) by parallelizing the TEE-based filters. As a practical deployment model, we suggest that Internet exchange points (IXPs) are the ideal candidates for the early adopters of our verifiable filters due to their central locations and flexible software-defined architecture.

cs.CR↗

arXiv2018

Neuro-Symbolic Execution: The Feasibility of an Inductive Approach to Symbolic Execution

Symbolic execution is a powerful technique for program analysis. However, it has many limitations in practical applicability: the path explosion problem encumbers scalability, the need for language-specific implementation, the inability to handle complex dependencies, and the limited expressiveness of theories supported by underlying satisfiability checkers. Often, relationships between variables of interest are not expressible directly as purely symbolic constraints. To this end, we present a new approach -- neuro-symbolic execution -- which learns an approximation of the relationship as a neural net. It features a constraint solver that can solve mixed constraints, involving both symbolic expressions and neural network representation. To do so, we envision such constraint solving as procedure combining SMT solving and gradient-based optimization. We demonstrate the utility of neuro-symbolic execution in constructing exploits for buffer overflows. We report success on 13/14 programs which have difficult constraints, known to require specialized extensions to symbolic execution. In addition, our technique solves $100$\% of the given neuro-symbolic constraints in $73$ programs from standard verification and invariant synthesis benchmarks.

cs.PL↗

arXiv2016

Preventing Your Faults From Telling Your Secrets: Defenses Against Pigeonhole Attacks

New hardware primitives such as Intel SGX secure a user-level process in presence of an untrusted or compromised OS. Such "enclaved execution" systems are vulnerable to several side-channels, one of which is the page fault channel. In this paper, we show that the page fault side-channel has sufficient channel capacity to extract bits of encryption keys from commodity implementations of cryptographic routines in OpenSSL and Libgcrypt --- leaking 27% on average and up to 100% of the secret bits in many case-studies. To mitigate this, we propose a software-only defense that masks page fault patterns by determinising the program's memory access behavior. We show that such a technique can be built into a compiler, and implement it for a subset of C which is sufficient to handle the cryptographic routines we study. This defense when implemented generically can have significant overhead of up to 4000X, but with help of developer-assisted compiler optimizations, the overhead reduces to at most 29.22% in our case studies. Finally, we discuss scope for hardware-assisted defenses, and show one solution that can reduce overheads to 6.77% with support from hardware changes.

cs.CR↗