HVLearn: Automated Black-Box Analysis of Hostname Verification in SSL/TLS Implementations

Sivakorn, Suphannee; Argyros, George; Pei, Kexin; Keromytis, Angelos D.; Jana, Suman

doi:10.1109/sp.2017.46

Cited by 59 publications

(26 citation statements)

References 16 publications

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“…Sivakorn et al [52] proposed a black-box hostname verification testing framework for TLS libraries and applications. They evaluated the hostnames accepted by seven TLS libraries and applications and found eight violations, including: invalid hostname characters, incorrect null characters parsing, and incorrect wildcard parsing.…”

Section: Related Workmentioning

confidence: 99%

The Sorry State of TLS Security in Enterprise Interception Appliances

2020

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Network traffic inspection, including TLS traffic, in enterprise environments is widely practiced. Reasons for doing so are primarily related to improving enterprise security (e.g., phishing and malicious traffic detection) and meeting legal requirements (e.g., preventing unauthorized data leakage and copyright violations). To analyze TLS-encrypted data, network appliances implement a Man-in-the-Middle (MITM) TLS proxy by acting as the intended web server to a requesting client (e.g., a browser) and acting as the client to the actual/outside web server. As such, the TLS proxy must implement both a TLS client and a server and handle a large amount of traffic, preferably in real-time. However, as protocol and implementation layer vulnerabilities in TLS/HTTPS are quite frequent, these proxies must be at least as secure as a modern, up-to-date web browser and a properly configured web server (e.g., an A+ rating in SSLlabs.com). As opposed to client-end TLS proxies (e.g., as in several anti-virus products), the proxies in network appliances may serve hundreds to thousands of clients, and any vulnerability in their TLS implementations can significantly downgrade enterprise security. To analyze TLS security of network appliances, we develop a comprehensive framework, combining and extending tests from existing work on client-end and network-based interception studies. We analyze 13 representative network appliances over a period of more than a year (including versions before and after notifying affected vendors, a total of 17 versions) and uncover several security issues. For instance, we found that four appliances perform no certificate validation at all, three use pre-generated certificates, and eleven accept certificates signed using MD5, exposing their clients to MITM attacks. Our goal is to highlight the risks introduced by widely used TLS proxies in enterprise and government environments, potentially affecting many systems hosting security, privacy, and financially sensitive data.

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Section: Related Workmentioning

confidence: 99%

The Sorry State of TLS Security in Enterprise Interception Appliances

2020

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“…Analysis assuming known protocol format: Other approaches [2,27,36] perform protocol and device analysis assuming protocol format is known. For instance, Alembic [27] tackled the problem of inferring the stateful model in a form of a finite state machine (FSM) of network functions (e.g., firewalls, NAT).…”

Section: Prior Work and Limitationsmentioning

confidence: 99%

Towards Oblivious Network Analysis using Generative Adversarial Networks

Lin

Moon

Zarate

et al. 2019

Proceedings of the 18th ACM Workshop on Hot Topics in Networks

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Modern systems across diverse application domains (e.g., IoT, automotive) have many black-box devices whose internal structures and/or protocol formats are unknown. We currently lack the tools to systematically understand the behavior and learn the security weaknesses of these black-box devices. Such tools could enable many use cases, such as: 1) identifying input packets that lead to network attacks; and 2) inferring the format of unknown protocols. Our goal is to enable oblivious network analysis which can perform the aforementioned tasks for black-box devices. In this work, we explore the use of a recent machine learning tool called generative adversarial networks (GANs) [16] to enable this vision. Unlike other competing approaches, GANs can work in a truly black-box setting and can infer complex dependencies between protocol fields with little to no supervision. We leverage GANs to show the preliminary use cases of our approaches using two case studies: 1) generating synthetic protocol messages given only samples of messages; and 2) generating attack inputs for a black-box system. While there are still many open challenges, our results suggest the early

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“…Differential fuzzing has been successfully applied before for finding bugs and vulnerabilities in a variety of applications, such as LF and XZ parsers, PDF viewers, SSL/TLS libraries, and C compilers [36], [38], [41]. However, to the best of our knowledge, we are the first to explore differential fuzzing for side-channel analysis.…”

Section: Introductionmentioning

confidence: 99%

DifFuzz: Differential Fuzzing for Side-Channel Analysis

Nilizadeh

Noller

Păsăreanu

2019

2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)

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Side-channel attacks allow an adversary to uncover secret program data by observing the behavior of a program with respect to a resource, such as execution time, consumed memory or response size. Side-channel vulnerabilities are difficult to reason about as they involve analyzing the correlations between resource usage over multiple program paths. We present DIFFUZZ, a fuzzing-based approach for detecting side-channel vulnerabilities related to time and space. DIFFUZZ automatically detects these vulnerabilities by analyzing two versions of the program and using resource-guided heuristics to find inputs that maximize the difference in resource consumption between secretdependent paths. The methodology of DIFFUZZ is general and can be applied to programs written in any language. For this paper, we present an implementation that targets analysis of JAVA programs, and uses and extends the KELINCI and AFL fuzzers. We evaluate DIFFUZZ on a large number of JAVA programs and demonstrate that it can reveal unknown sidechannel vulnerabilities in popular applications. We also show that DIFFUZZ compares favorably against BLAZER and THEMIS, two state-of-the-art analysis tools for finding side-channels in JAVA programs.

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HVLearn: Automated Black-Box Analysis of Hostname Verification in SSL/TLS Implementations

Cited by 59 publications

References 16 publications

The Sorry State of TLS Security in Enterprise Interception Appliances

The Sorry State of TLS Security in Enterprise Interception Appliances

Towards Oblivious Network Analysis using Generative Adversarial Networks

DifFuzz: Differential Fuzzing for Side-Channel Analysis

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