Critical BlueFrag Bluetooth Vulnerability In Older Android OS

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Abstract: With the wide application and deployment of cloud computing in enterprises, virtualization developers and security researchers are paying more attention to cloud computing security. The core component of cloud computing products is the hypervisor, which is also known as the virtual machine monitor (VMM) that can isolate multiple virtual machines in one host machine. However, compromising the hypervisor can lead to virtual machine escape and the elevation of privilege, allowing attackers to gain the permission of code execution in the host. Therefore, the security analysis and vulnerability detection of the hypervisor are critical for cloud computing enterprises. Importantly, virtual devices expose many interfaces to a guest user for communication, making virtual devices the most vulnerable part of a hypervisor. However, applying fuzzing to the virtual devices of a hypervisor is challenging because the data structures transferred by DMA are constructed in a nested form according to protocol specifications. Failure to understand the protocol of the virtual devices will make the fuzzing process stuck in the initial fuzzing stage, resulting in inefficient fuzzing.

Abstract: Existing mutation based fuzzers tend to randomly mutate the input of a program without understanding its underlying syntax and semantics. In this paper, we propose a novel on-the-fly probing technique (called ProFuzzer) that automatically recovers and understands input fields of critical importance to vulnerability discovery during a fuzzing process and intelligently adapts the mutation strategy to enhance the chance of hitting zero-day targets. Since such probing is transparently piggybacked to the regular fuzzing, no prior knowledge of the input specification is needed. During fuzzing, individual bytes are first mutated and their fuzzing results are automatically analyzed to link those related together and identify the type for the field connecting them; these bytes are further mutated together following type-specific strategies, which substantially prunes the search space. We define the probe types generally across all applications, thereby making our technique application agnostic. Our experiments on standard benchmarks and real-world applications show that ProFuzzer substantially outperforms AFL and its optimized version AFLFast, as well as other state-of-art fuzzers including VUzzer, Driller and QSYM. Within two months, it exposed 42 zero-days in 10 intensively tested programs, generating 30 CVEs.

Abstract: Uncontrolled memory consumption is a kind of critical software security weaknesses.It can also become a security-critical vulnerability when attackers can control the input to consume a large amount of memory to launch a Denial-of-Service attack. However, detecting such vulnerability is challenging due to the fact that it requires long executions with well-crafted inputs to trigger excessive memory consumption, while the state-of-the-art testing techniques have mostly focused on code coverage.To tackle this challenge, we propose a feedback-directed fuzzing technique, named MemLock, to automatically generate those memory-consuming inputs to trigger memory consumption bugs.The fuzzing process is guided with memory consumption information so that the approach is general and does not require any domain knowledge.We perform a thorough evaluation for MemLock on 14 widely-used real-world programs.Our experiment results show that MemLock substantially outperforms the state-of-the-art fuzzing techniques, including AFL, AFLfast, PerfFuzz, FairFuzz and QSYM, in discovering memory consumption bugs.During the experiments, we discovered several previously unknown excessive memory consumption vulnerabilities and received 15 new CVEs.

Abstract: Decentralized cryptocurrencies feature the use of blockchain to transfer values among peers on networks without central agency. Smart contracts are programs running on top of the blockchain consensus protocol to enable people make agreements while minimizing trusts. Millions of smart contracts have been deployed in various decentralized applications. The security vulnerabilities within those smart contracts pose significant threats to their applications. Indeed, many critical security vulnerabilities within smart contracts on Ethereum platform have caused huge financial losses to their users. In this work, we present ContractFuzzer, a novel fuzzer to test Ethereum smart contracts for security vulnerabilities. ContractFuzzer generates fuzzing inputs based on the ABI specifications of smart contracts, defines test oracles to detect security vulnerabilities, instruments the EVM to log smart contracts runtime behaviors, and analyzes these logs to report security vulnerabilities. Our fuzzing of 6991 smart contracts has flagged more than 459 vulnerabilities with high precision. In particular, our fuzzing tool successfully detects the vulnerability of the DAO contract that leads to USD 60 million loss and the vulnerabilities of Parity Wallet that have led to the loss of USD 30 million and the freezing of USD 150 million worth of Ether.

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.

BlueFrag: February's Android security release included a fix for a bug which \"could enable a remote attacker using a specially crafted transmission to execute arbitrary code within the context of a privileged process.\" The bug was marked as a \"moderate\" denial of service on Android 10 and a \"critical\" remote code execution bug on Android 8 and 9. The researchers who found the bug have now written up the attack, which they call \"BlueFrag.\" Briefly, it's a combination of two bugs, one in the Bluetooth driver and one in a core Android component called Bionic, and the clinical description in the February bulletin underplays the severity: if you have Bluetooth enabled, anyone in range of your phone could silently start running code on it, if you're on Android 8 and 9. The Bluetooth bug was still there in Android 10 until February, but changes in Bionic mean that the worst they can do is cause the Bluetooth service to crash. Apart from (of course) once again asking you to take software updates, we think this is a good example of why it's worth staying on the newest versions of software whenever possible: while security releases for older versions of software tend to only address known, identified security bugs, newer versions often have bug fixes in general that improve the sturdiness of the code and make it harder for newly-discovered bugs to be exploitable. 153554b96e

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