Biblio

Code-reuse attacks (including ROP/JOP) severely threaten computer security. Control-flow integrity (CFI), which can restrict control flow in legal scope, is recognised as an effective defence mechanism against code-reuse attacks. Hardware-based CFI uses Instruction Set Architecture (ISA) extensions with additional hardware modules to implement CFI and achieve better performance. However, hardware-based fine-grained CFI adds new instructions to the ISA, which can not be executed on old processors and breaks the compatibility of programs. Some coarse-grained CFI designs, such as Intel IBT, maintain the compatibility of programs but can not provide enough security guarantees.To balance the security and compatibility of hardware CFI, we propose Transparent Forward CFI (TFCFI). TFCFI implements hardware-based fine-grained CFI designs without changing the ISA. The software modification of TFCFI utilizes address information and hint instructions in RISC-V as transparent labels to mark the program. The hardware module of TFCFI monitors the control flow during execution. The program modified by TFCFI can be executed on old processors without TFCFI. Benefiting from transparent labels, TFCFI also solves the destination equivalence problem. The experiment on FPGA shows that TFCFI incurs negligible performance overhead (1.82% on average).

Return address authentication mechanisms protect return addresses by calculating and checking their message authentication codes (MACs) at runtime. However, these works only provide empirical analysis on their security, and it is still unclear whether the attacker can bypass these defenses by launching reuse attacks.In this paper, we present a solution to quantitatively analysis the security of return address authentication mechanisms against reuse attacks. Our solution utilizes some libc functions that could leakage data from memory. First, we perform reaching definition analysis to identify the source of parameters of these functions. Then we infer how many MACs could be observed at runtime by modifying these parameters. Afterward, we select the gadgets that could be exploited by reusing these observed MACs. Finally, we stitch desired gadget to craft attacks. We evaluated our solution on 5 real-word applications and successfully crafted reuse attacks on 3 of them. We find that the larger an application is, the more libc functions and gadgets can be found and reused, and furthermore, the more likely the attack is successfully crafted.

Return-oriented programming(ROP) is a prevalent technique that targets return addresses to hijack control flow. To prevent such attack, researchers mainly focus on either Shadow Stack or MAC-based mechanisms(message code authentication). But Shadow Stack suffers from additional memory overhead and information leakage, while MAC-based mechanisms(e.g. Zipper Stack) impose high runtime overhead for MAC calculations.In this paper, we propose Twine Stack, a hybrid and efficient return address protection mechanism with lightweight hardware extension. It utilizes a tiny hardware shadow stack to realize a new multi-chain Zipper Stack. Specifically, each entry in the shadow stack stores a return address and its MAC in each chain, allowing queueing calculation with just one hash module. At meantime, some return address verifications could be done by comparison with the hardware shadow stack, instead of calculation again. We implemented Twine Stack on RISC-V architecture, and evaluated it on FPGA board. Our experiments show that Twine Stack reduces over 95% hash verifications, and imposes merely 1.38% performance overhead with an area overhead of 974 LUTs and 726 flip flops. The result demonstrates that our hybrid scheme mitigates the drawbacks of each separate scheme.

Discovering the potential vulnerabilities in software plays a crucial role in ensuring the security of computer system. This paper proposes a method that can assist security auditors with the analysis of source code. When security auditors identify new vulnerabilities, our method can be adopted to make a list of recommendations that may have the same vulnerabilities for the security auditors. Our method relies on graph representation to automatically extract the mode of PDG(program dependence graph, a structure composed of control dependence and data dependence). Besides, it can be applied to the vulnerability extrapolation scenario, thus reducing the amount of audit code. We worked on an open-source vulnerability test set called Juliet. According to the evaluation results, the clustering effect produced is satisfactory, so that the feature vectors extracted by the Graph2Vec model are applied to labeling and supervised learning indicators are adopted to assess the model for its ability to extract features. On a total of 12,000 small data sets, the training score of the model can reach up to 99.2%, and the test score can reach a maximum of 85.2%. Finally, the recommendation effect of our work is verified as satisfactory.