Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCode-less Patching for Heap Vulnerabilities Using Targeted Calling Context Encoding
195
0
0.0
(
0
)
Added by
Lannan Luo
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Exploitation of heap vulnerabilities has been on the rise, leading to many devastating attacks. Conventional heap patch generation is a lengthy procedure, requiring intensive manual efforts. Worse, fresh patches tend to harm system dependability, hence deterring users from deploying them. We propose a heap patching system that simultaneously has the following prominent advantages: (1) generating patches without manual efforts; (2) installing patches without altering the code (so called code-less patching); (3) handling various heap vulnerability types; (4) imposing a very low overhead; and (5) no dependency on specific heap allocators. As a separate contribution, we propose targeted calling context encoding, which is a suite of algorithms for optimizing calling context encoding, an important technique with applications in many areas. The system properly combines heavyweight offline attack analysis with lightweight online defense generation, and provides a new countermeasure against heap attacks. The evaluation shows that the system is effective and efficient.
rate research
Read More
This paper presents an evaluation of the code representation model Code2vec when trained on the task of detecting security vulnerabilities in C source code. We leverage the open-source library astminer to extract path-contexts from the abstract syntax trees of a corpus of labeled C functions. Code2vec is trained on the resulting path-contexts with the task of classifying a function as vulnerable or non-vulnerable. Using the CodeXGLUE benchmark, we show that the accuracy of Code2vec for this task is comparable to simple transformer-based methods such as pre-trained RoBERTa, and outperforms more naive NLP-based methods. We achieved an accuracy of 61.43% while maintaining low computational requirements relative to larger models.
Since 2016, all of four major U.S. operators have rolled out nationwide Wi-Fi calling services. They are projected to surpass VoLTE (Voice over LTE) and other VoIP services in terms of mobile IP voice usage minutes in 2018. They enable mobile users to place cellular calls over Wi-Fi networks based on the 3GPP IMS (IP Multimedia Subsystem) technology. Compared with conventional cellular voice solutions, the major difference lies in that their traffic traverses untrustful Wi-Fi networks and the Internet. This exposure to insecure networks may cause the Wi-Fi calling users to suffer from security threats. Its security mechanisms are similar to the VoLTE, because both of them are supported by the IMS. They include SIM-based security, 3GPP AKA (Authentication and Key Agreement), IPSec (Internet Protocol Security), etc. However, are they sufficient to secure Wi-Fi calling services? Unfortunately, our study yields a negative answer. We conduct the first study of exploring security issues of the operational Wi-Fi calling services in three major U.S. operators networks using commodity devices. We disclose that current Wi-Fi calling security is not bullet-proof and uncover four vulnerabilities which stem from improper standard designs, device implementation issues and network operation slips. By exploiting the vulnerabilities, together with several state-of-the-art computer visual recognition technologies, we devise two proof-of-concept attacks: user privacy leakage and telephony harassment or denial of voice service (THDoS); both of them can bypass the security defenses deployed on mobile devices and the network infrastructure. We have confirmed their feasibility and simplicity using real-world experiments, as well as assessed their potential damages and proposed recommended solutions.
We uncover privacy vulnerabilities in the ICAO 9303 standard implemented by ePassports worldwide. These vulnerabilities, confirmed by ICAO, enable an ePassport holder who recently passed through a checkpoint to be reidentified without opening their ePassport. This paper explains how bisimilarity was used to discover these vulnerabilities, which exploit the BAC protocol - the original ICAO 9303 standard ePassport authentication protocol - and remains valid for the PACE protocol, which improves on the security of BAC in the latest ICAO 9303 standards. In order to tackle such bisimilarity problems, we develop here a chain of methods for the applied $pi$-calculus including a symbolic under-approximation of bisimilarity, called open bisimilarity, and a modal logic, called classical FM, for describing and certifying attacks. Evidence is provided to argue for a new scheme for specifying such unlinkability problems that more accurately reflects the capabilities of an attacker.
Binary code similarity detection is a fundamental technique for many security applications such as vulnerability search, patch analysis, and malware detection. There is an increasing need to detect similar code for vulnerability search across architectures with the increase of critical vulnerabilities in IoT devices. The variety of IoT hardware architectures and software platforms requires to capture semantic equivalence of code fragments in the similarity detection. However, existing approaches are insufficient in capturing the semantic similarity. We notice that the abstract syntax tree (AST) of a function contains rich semantic information. Inspired by successful applications of natural language processing technologies in sentence semantic understanding, we propose a deep learning-based AST-encoding method, named ASTERIA, to measure the semantic equivalence of functions in different platforms. Our method leverages the Tree-LSTM network to learn the semantic representation of a function from its AST. Then the similarity detection can be conducted efficiently and accurately by measuring the similarity between two representation vectors. We have implemented an open-source prototype of ASTERIA. The Tree-LSTM model is trained on a dataset with 1,022,616 function pairs and evaluated on a dataset with 95,078 function pairs. Evaluation results show that our method outperforms the AST-based tool Diaphora and the-state-of-art method Gemini by large margins with respect to the binary similarity detection. And our method is several orders of magnitude faster than Diaphora and Gemini for the similarity calculation. In the application of vulnerability search, our tool successfully identified 75 vulnerable functions in 5,979 IoT firmware images.
We have developed a one-dimensional code to solve ultra-relativistic hydrodynamic problems, using the Glimm method for an accurate treatment of shocks and contact discontinuities. The implementation of the Glimm method is based on an exact Riemann solver and van der Corput sampling sequence. In order to improve computational efficiency, the Glimm method is replaced by a finite differencing scheme in those regions where the fluid flow is sufficiently smooth. The accuracy and convergence of this hybrid method is investigated in tests involving planar, cylindrically and spherically symmetric flows that exhibit strong shocks and Lorentz factors of up to ~2000. This hybrid code has proven to be successful in simulating the interaction between a thin, ultra-relativistic, spherical shell and a low density stationary medium, a situation likely to appear in Gamma-Ray Bursts, supernovae explosions, pulsar winds and AGNs.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
