Abstract

With a growing demand of concurrent software to exploit multi-core hardware capability, concurrency vulnerabilities have become an inevitable threat to the security of today’s IT industry. Existing concurrent program detection schemes focus mainly on detecting concurrency errors such as data races, atomicity violation, etc., with little attention paid to detect concurrency vulnerabilities that may be exploited to infringe security. In this paper, we propose a heuristic framework that combines both static analysis and fuzz testing to detect targeted concurrency vulnerabilities such as concurrency buffer overflow, double free, and use-after-free. The static analysis locates sensitive concurrent operations in a concurrent program, categorizes each finding into a potential type of concurrency vulnerability, and determines the execution order of the sensitive operations in each finding that would trigger the suspected concurrency vulnerability. The results are then plugged into the fuzzer with the execution order fixed by the static analysis in order to trigger the suspected concurrency vulnerabilities.

In order to introduce more variance which increases possibility that the concurrency errors can be triggered, we also propose manipulation of thread scheduling priority to enable a fuzzer such as AFL to effectively explore thread interleavings in testing a concurrent program. To the best of our knowledge, this is the first fuzzer that is capable of effectively exploring concurrency errors.

In evaluating the proposed heuristic framework with a benchmark suit of six real-world concurrent C programs, the framework detected two concurrency vulnerabilities for the proposed concurrency vulnerability detection, both being confirmed to be true positives, and produced three new crashes for the proposed interleaving exploring fuzzer that existing fuzzers could not produce. These results demonstrate the power and effectiveness of the proposed heuristic framework in detecting concurrency errors and vulnerabilities.

Abstract

Privacy-preserving processing is desirable for cloud computing to relieve users’ concern of loss of control of their uploaded data. This may be fulfilled with homomorphic encryption. With widely used JPEG, it is desirable to enable JPEG decompression in the homomorphic encryption domain. This is a great challenge since JPEG decoding needs to determine a matched codeword, which then extracts a codeword-dependent number of coefficients. With no access to the information of encrypted content, a decoder does not know which codeword is matched, and thus cannot tell how many coefficients to extract, not to mention to compute their values. In this paper, we propose a novel scheme that enables JPEG decompression in the homomorphic encryption domain. The scheme applies a statically controlled iterative procedure to decode one coefficient per iteration. In one iteration, each codeword is compared with the bitstream to compute an encrypted Boolean that represents if the codeword is a match or not. Each codeword would produce an output coefficient and generate a new bitstream by dropping consumed bits as if it were a match. If a codeword is associated with more than one coefficient, the codeword is replaced with the codeword representing the remaining undecoded coefficients for the next decoding iteration. The summation of each codeword’s output multiplied by its matching Boolean is the output of the current iteration. This is equivalent to selecting the output of a matched codeword. A side benefit of our statically controlled decoding procedure is that paralleled Single-Instruction Multiple-Data (SIMD) is fully supported, wherein multiple plaintexts are encrypted into a single plaintext, and decoding a ciphertext block corresponds to decoding all corresponding plaintext blocks. SIMD also reduces the total size of ciphertexts of an image. Experimental results are reported to show the performance of our proposed scheme.