Feedback-driven side-channel analysis for networked applications

Kadron, Ismet Burak; Rosner, Nicolás; Bultan, Tevfik

doi:10.1145/3395363.3397365

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Dynamic Analysis for Side Channels. Dynamic analysis has been used for analyzing side channels [16,27,34]. Tizpaz-Niari et al [34] developed a data-driven approach to debug timing side channels.…”

Section: Related Workmentioning

confidence: 99%

“…There are known practical side-channel attacks against the RSA algorithm [7], an online health system [10], the Google's Keyczar Library [24], and the Xbox 360 [37]. In the Xbox 360, timing side channels allowed attackers to reduce the maximum number of trials to compromise a 16 byte secret from 256 16 to 256 * 16 due to vulnerable implementations in byte array comparisons. Another example is Spectre [20] that challenged the confidentiality of computer devices via side channels.…”

Section: Introductionmentioning

confidence: 99%

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QFuzz: quantitative fuzzing for side channels

Noller

Tizpaz-Niari

2021

Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis

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Side channels pose a significant threat to the confidentiality of software systems. Such vulnerabilities are challenging to detect and evaluate because they arise from non-functional properties of software such as execution times and require reasoning on multiple execution traces. Recently, noninterference notions have been adapted in static analysis, symbolic execution, and greybox fuzzing techniques. However, noninterference is a strict notion and may reject security even if the strength of information leaks are weak. A quantitative notion of security allows for the relaxation of noninterference and tolerates small (unavoidable) leaks. Despite progress in recent years, the existing quantitative approaches have scalability limitations in practice.In this work, we present QFuzz, a greybox fuzzing technique to quantitatively evaluate the strength of side channels with a focus on min entropy. Min entropy is a measure based on the number of distinguishable observations (partitions) to assess the resulting threat from an attacker who tries to compromise secrets in one try. We develop a novel greybox fuzzing equipped with two partitioning algorithms that try to maximize the number of distinguishable observations and the cost differences between them.We evaluate QFuzz on a large set of benchmarks from existing work and real-world libraries (with a total of 70 subjects). QFuzz compares favorably to three state-of-the-art detection techniques. QFuzz provides quantitative information about leaks beyond the capabilities of all three techniques. Crucially, we compare QFuzz to a state-of-the-art quantification tool and find that QFuzz significantly outperforms the tool in scalability while maintaining similar precision. Overall, we find that our approach scales well for real-world applications and provides useful information to evaluate resulting threats. Additionally, QFuzz identifies a zero-day side-channel vulnerability in a security critical Java library that has since been confirmed and fixed by the developers. CCS CONCEPTS• Security and privacy → Software security engineering; • Software and its engineering → Software testing and debugging.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QFuzz: quantitative fuzzing for side channels

Noller

Tizpaz-Niari

2021

Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis

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“…Observe that computing p σ is equivalent to the problem of model counting, which seeks to compute the number of solutions of a given formula. Therefore, the exact techniques require O(2 m ) model-counting queries [13,27,39]; therefore, such techniques often do not scale for large values of m. Accordingly, the state of the art often relies on sampling-based techniques that perform well in practice but can only provide lower or upper bounds on the entropy [37,49]. As is often the case, techniques that only guarantee lower or upper bounds can output estimates that can be arbitrarily far from the ground truth.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Shannon Entropy Estimator

Golia

Juba

Meel

2022

Computer Aided Verification

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Quantified information flow (QIF) has emerged as a rigorous approach to quantitatively measure confidentiality; the information-theoretic underpinning of QIF allows the end-users to link the computed quantities with the computational effort required on the part of the adversary to gain access to desired confidential information. In this work, we focus on the estimation of Shannon entropy for a given program $$\varPi $$ Π . As a first step, we focus on the case wherein a Boolean formula $$\varphi (X,Y)$$ φ ( X , Y ) captures the relationship between inputs X and output Y of $$\varPi $$ Π . Such formulas $$\varphi (X,Y)$$ φ ( X , Y ) have the property that for every valuation to X, there exists exactly one valuation to Y such that $$\varphi $$ φ is satisfied. The existing techniques require $$\mathcal {O}(2^m)$$ O ( 2 m ) model counting queries, where $$m = |Y|$$ m = | Y | .We propose the first efficient algorithmic technique, called $$\mathsf {EntropyEstimation}$$ EntropyEstimation to estimate the Shannon entropy of $$\varphi $$ φ with PAC-style guarantees, i.e., the computed estimate is guaranteed to lie within a $$(1\pm \varepsilon )$$ ( 1 ± ε ) -factor of the ground truth with confidence at least $$1-\delta $$ 1 - δ . Furthermore, $$\mathsf {EntropyEstimation}$$ EntropyEstimation makes only $$\mathcal {O}(\frac{min(m,n)}{\varepsilon ^2})$$ O ( m i n ( m , n ) ε 2 ) counting and sampling queries, where $$m = |Y|$$ m = | Y | , and $$n = |X|$$ n = | X | , thereby achieving a significant reduction in the number of model counting queries. We demonstrate the practical efficiency of our algorithmic framework via a detailed experimental evaluation. Our evaluation demonstrates that the proposed framework scales to the formulas beyond the reach of the previously known approaches.

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