SGXElide: enabling enclave code secrecy via self-modification

Bauman, Erick; Wang, Huibo; Zhang, Mingwei; Liu, Zhiqiang

doi:10.1145/3179541.3168833

Cited by 6 publications

(12 citation statements)

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“…SGX-BigMatrix [60] is a Python-based secure and oblivious data analytic system for matrix computation. SGXElide [20] loads encrypted code at runtime to provide confidentiality of enclave code, but it does not tackle dynamic loaded code as in Uranus. JIT-Guard [33] uses SGX to protect a JIT compiler, not applications.…”

Section: Related Workmentioning

confidence: 99%

Uranus

Jiang

Chen

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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Applications written in Java have strengths to tackle diverse threats in public clouds, but these applications are still prone to privileged attacks when processing plaintext data. Intel SGX is powerful to tackle these attacks, and traditional SGX systems rewrite a Java application's sensitive functions, which process plaintext data, using C/C++ SGX API. Although this code-rewrite approach achieves good efficiency and a small TCB, it requires SGX expert knowledge and can be tedious and error-prone. To tackle the limitations of rewriting Java to C/C++, recent SGX systems propose a code-reuse approach, which runs a default JVM in an SGX enclave to execute the sensitive Java functions. However, both recent study and this paper find that running a default JVM in enclaves incurs two major vulnerabilities, Iago attacks, and control flow leakage of sensitive functions, due to the usage of OS features in JVM. In this paper, Uranus creates easy-to-use Java programming abstractions for application developers to annotate sensitive functions, and Uranus automatically runs these functions in SGX at runtime. Uranus effectively tackles the two major vulnerabilities in the code-reuse approach by presenting two new protocols: 1) a Java bytecode attestation protocol for dynamically loaded functions; and 2) an OS-decoupled, efficient GC protocol optimized for data-handling applications running in enclaves. We implemented Uranus in Linux and applied it to two diverse data-handling applications: Spark and ZooKeeper. Evaluation shows that: 1) Uranus achieves the same security guarantees as two relevant SGX systems for these two applications with only a few annotations; 2) Uranus has reasonable performance overhead compared to the native, insecure applications; and 3) Uranus defends against privileged attacks. Uranus source code and evaluation results are released on https://github.com/hku-systems/uranus. CCS CONCEPTS • Security and privacy → Software security engineering.

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Section: Related Workmentioning

confidence: 99%

Uranus

Jiang

Chen

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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“…The goal of an enclave is to prevent the leakage of its sensitive information (data or/and code) both at runtime and at rest. Thus, code secrecy mechanism [8] may be used by an enclave to protect its private code. • From the host application's view It considers the enclave as untrusted [26,35].…”

Section: Threat Modelmentioning

confidence: 99%

“…Besides, the dynamically loaded and runtime-generated code make the problem even harder. Some enclaves may load or generate code at runtime for the purpose of code secrecy [8]. For instance, an enclave developer may want to protect its proprietary code.…”

Section: Ci-2)mentioning

confidence: 99%

“…Current solutions and their limitations Because of the isolation provided by SGX, an enclave is prevented from the inspection by the anti-virus system at runtime [13]. Besides, the code in an enclave may be deployed in the ciphertext and only decrypted inside enclave at runtime to protect the code secrecy [8]. This further impedes introducing a full vetting process on the enclave code.…”

Section: Introductionmentioning

confidence: 99%

“…We need to ensure such instructions cannot be abused by the enclave to bypass our solution. Moreover, the hardware isolation of SGX makes it even harder when the code secrecy of an enclave is protected [8], since the code inside the enclave cannot be vetted by the host application.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Efficiently Establishing Mutual Distrust Between Host Application and Enclave for SGX

Chen,

Li,

et al. 2020

Preprint

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Since its debut, SGX has been used in many applications, e.g., secure data processing. However, previous systems usually assume a trusted enclave and ignore the security issues caused by an untrusted enclave. For instance, a vulnerable (or even malicious) third-party enclave can be exploited to attack the host application and the rest of the system. In this paper, we propose an efficient mechanism to confine an untrusted enclave's behaviors. The threats of an untrusted enclave come from the enclave-host asymmetries. They can be abused to access arbitrary memory regions of its host application, jump to any code location after leaving the enclave and forge the stack register to manipulate the saved context. Our solution breaks such asymmetries and establishes mutual distrust between the host application and the enclave. It leverages Intel MPK for efficient memory isolation and the x86 single-step debugging mechanism to capture the event when an enclave is exiting. Then it performs the integrity check for the jump target and the stack pointer. We have solved two practical challenges and implemented a prototype system. The evaluation with multiple micro-benchmarks and representative real-world applications demonstrated the efficiency of our system, with less than 4% performance overhead.

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MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

Hanzlik¹,

Zhang²,

Grosse³

et al. 2018

Preprint

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With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model.In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference.

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SGXElide: enabling enclave code secrecy via self-modification

Cited by 6 publications

References 0 publications

Uranus

Uranus

Towards Efficiently Establishing Mutual Distrust Between Host Application and Enclave for SGX

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

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