Memory access integrity:detecting fine-grained memory access errors in binary code

As one of the most notorious programming errors,memory access errors still hurt modern software security.Particularly,they are hidden deeply in important software systems written in memory unsafe languages like C/C++.Plenty of work have been proposed to detect bugs leading to memory access errors.However,all existing works lack the ability to handle two challenges.First,they are not able to tackle fine-grained memory access errors,e.g.,data overflow inside one data structure.These errors are usually overlooked for a long time since they happen inside one memory block and do not lead to program crash.Second,most existing works rely on source code or debugging information to recover memory boundary information,so they cannot be directly applied to detection of memory access errors in binary code.However,searching memory access errors in binary code is a very common scenario in software vulnerability detection and exploitation.In order to overcome these challenges,we propose Memory Access Integrity(MAI),a dynamic method to detect finegrained memory access errors in off-the-shelf binary executables.The core idea is to recover fine-grained accessing policy between memory access behaviors and memory ranges,and then detect memory access errors based on the policy.The key insight in our work is that memory accessing patterns reveal information for recovering the boundary of memory objects and the accessing policy.Based on these recovered information,our method maintains a new memory model to simulate the life cycle of memory objects and report errors when any accessing policy is violated.We evaluate our tool on popular CTF datasets and real world softwares.Compared with the state of the art detection tool,the evaluation result demonstrates that our tool can detect fine-grained memory access errors effectively and efficiently.As the practical impact,our tool has detected three 0-day memory access errors in an audio decoder.

Memory access integrity:detecting fine-grained memory access errors in binary code

As one of the most notorious programming errors,memory access errors still hurt modern software security.Particularly,they are hidden deeply in important software systems written in memory unsafe languages like C/C++.Plenty of work have been proposed to detect bugs leading to memory access errors.However,all existing works lack the ability to handle two challenges.First,they are not able to tackle fine-grained memory access errors,e.g.,data overflow inside one data structure.These errors are usually overlooked for a long time since they happen inside one memory block and do not lead to program crash.Second,most existing works rely on source code or debugging information to recover memory boundary information,so they cannot be directly applied to detection of memory access errors in binary code.However,searching memory access errors in binary code is a very common scenario in software vulnerability detection and exploitation.In order to overcome these challenges,we propose Memory Access Integrity(MAI),a dynamic method to detect finegrained memory access errors in off-the-shelf binary executables.The core idea is to recover fine-grained accessing policy between memory access behaviors and memory ranges,and then detect memory access errors based on the policy.The key insight in our work is that memory accessing patterns reveal information for recovering the boundary of memory objects and the accessing policy.Based on these recovered information,our method maintains a new memory model to simulate the life cycle of memory objects and report errors when any accessing policy is violated.We evaluate our tool on popular CTF datasets and real world softwares.Compared with the state of the art detection tool,the evaluation result demonstrates that our tool can detect fine-grained memory access errors effectively and efficiently.As the practical impact,our tool has detected three 0-day memory access errors in an audio decoder.

On the Current Error Based Sampled-data Iterative Learning Control with Reduced Memory Capacity

The design of iterative learning controller(ILC) requires to store the system input, output or control parameters of previous trials for generating the input of the current trial. In order to apply the iterative learning controller for a real application and reduce the memory size for implementation, a current error based sampled-data proportional-derivative(PD) type iterative learning controller is proposed for control systems with initial resetting error, input disturbance and output measurement noise in this paper.The proposed iterative learning controller is simple and effective. The first contribution in this paper is to prove the learning error convergence via a rigorous technical analysis. It is shown that the learning error will converge to a residual set if a forgetting factor is introduced in the controller. All the theoretical results are also shown by computer simulations. The second main contribution is to realize the iterative learning controller by a digital circuit using a field programmable gate array(FPGA) chip applied to repetitive position tracking control of direct current(DC) motors. The feasibility and effectiveness of the proposed current error based sampleddata iterative learning controller are demonstrated by the experiment results. Finally, the relationship between learning performance and design parameters are also discussed extensively.