Abstract The exploitable heap layouts are used to determine the exploitability of heap vulnerabilities in general-purpose applications. Prior studies have focused on using fuzzing-based methods to generate more exploitable heap layouts. However, the exploitable heap layout cannot fully demonstrate the exploitability of a vulnerability, as it is uncertain whether the attacker can control the data covered by the overflow. In this paper, we propose the Heap Overflow Exploitability Evaluator (Hoee), a new approach to automatically reveal the exploitability of heap buffer overflow vulnerabilities by evaluating proof-of-concepts (PoCs) generated by fuzzers. Hoee leverages several techniques to collect dynamic information at runtime and recover heap object layouts in a fine-grained manner. The overflow context is carefully analyzed to determine whether the sensitive pointer is corrupted, tainted, or critically used. We evaluate Hoee on 34 real-world CVE vulnerabilities from 16 general-purpose programs. The results demonstrate that Hoee accurately identifies the key factors for developing exploits in vulnerable contexts and correctly recognizes the behavior of overflow.

A trusted execution environment (TEE) such as Intel Software Guard Extension (SGX) runs attestation to prove to a data owner the integrity of the initial state of an enclave, including the program to operate on her data. For this purpose, the data-processing program is supposed to be open to the owner or a trusted third party, so its functionality can be evaluated before trust being established. In the real world, however, increasingly there are application scenarios in which the program itself needs to be protected (e.g., proprietary algorithm). So its compliance with privacy policies as expected by the data owner should be verified without exposing its code. To this end, this paper presents Deflection, a new model for TEE-based delegated and flexible in-enclave code verification. Given that the conventional solutions do not work well under the resource-limited and TCB-frugal TEE, we come up with a new design inspired by Proof-Carrying Code. Our design strategically moves most of the workload to the code generator, which is responsible for producing easy-to-check code, while keeping the consumer simple. Also, the whole consumer can be made public and verified through a conventional attestation. We implemented this model on Intel SGX and demonstrate that it introduces a very small part of TCB. We also thoroughly evaluated its performance on micro- and macro- benchmarks and real-world applications, showing that the design only incurs a small overhead when enforcing several categories of security policies.

Recently Adversarial Examples (AEs) that deceive deep learning models have been a topic of intense research interest. Compared with the AEs in the digital space, the physical adversarial attack is considered as a more severe threat to the applications like face recognition in authentication, objection detection in autonomous driving cars, etc. In particular, deceiving the object detectors practically, is more challenging since the relative position between the object and the detector may keep changing. Existing works attacking object detectors are still very limited in various scenarios, e.g., varying distance and angles, etc. In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors. Particularly, for Hiding Attack (HA), we proposed thefeature-interference reinforcement (FIR) method and theenhanced realistic constraints generation (ERG) to enhance robustness, and for Appearing Attack (AA), we proposed thenested-AE, which combines two AEs together to attack object detectors in both long and short distance. We also designed diverse styles of AEs to make AA more surreptitious. Evaluation results show that our AEs can attack the state-of-the-art real-time object detectors (i.e., YOLO V3 and faster-RCNN) at the success rate up to 92.4% with varying distance from 1m to 25m and angles from -60º to 60º. Our AEs are also demonstrated to be highly transferable, capable of attacking another three state-of-the-art black-box models with high success rate.