We propose, BanditRepair, a system that systematically explores and assesses a set of possible runtime patches. The system is grounded on so-called bandit algorithms, that are online machine learning algorithms, designed for constantly balancing exploitation and exploration. BanditRepair's runtime patches are based on modifying the execution state for repairing null dereferences. BanditRepair constantly trades the ratio of automatically handled failures for searching for new runtime patches and vice versa. We evaluate the system with 16 null dereference field bugs, where BanditRepair identifies a total of 8460 different runtime patches, which are composed of 1 up to 8 decisions (execution modifications) taken in a row. We are the first to finely characterize the search space and the outcomes of runtime repair based on execution modification.
Berger and Zorn @cite_16 show that is possible to effectively tolerate memory errors and provide probabilistic memory safety by randomizing the memory allocation and providing memory replication. The work by @cite_23 exploits a specific hardware feature called ECC-memory for detecting illegal memory accesses at runtime. The idea of the paper is to use the consistency checks of the ECC-memory to detect illegal memory accesses (for instance due to buffer overflow). Both techniques are semantically equivalent in the normal case. On the contrary is meant to reason about the search space of execution modifications that are not semantically equivalent, where one taken decision can impact the rest of the computation.
@cite_16 @cite_23
2136938453 2098809490
Applications written in unsafe languages like C and C++ are vulnerable to memory errors such as buffer overflows, dangling pointers, and reads of uninitialized data. Such errors can lead to program crashes, security vulnerabilities, and unpredictable behavior. We present DieHard, a runtime system that tolerates these errors while probabilistically maintaining soundness. DieHard uses randomization and replication to achieve probabilistic memory safety by approximating an infinite-sized heap. DieHard's memory manager randomizes the location of objects in a heap that is at least twice as large as required. This algorithm prevents heap corruption and provides a probabilistic guarantee of avoiding memory errors. For additional safety, DieHard can operate in a replicated mode where multiple replicas of the same application are run simultaneously. By initializing each replica with a different random seed and requiring agreement on output, the replicated version of Die-Hard increases the likelihood of correct execution because errors are unlikely to have the same effect across all replicas. We present analytical and experimental results that show DieHard's resilience to a wide range of memory errors, including a heap-based buffer overflow in an actual application. Memory leaks and memory corruption are two major forms of software bugs that severely threaten system availability and security. According to the US-CERT vulnerability notes database, 68 of all reported vulnerabilities in 2003 were caused by memory leaks or memory corruption. Dynamic monitoring tools, such as the state-of-the-art Purify, are commonly used to detect memory leaks and memory corruption. However, most of these tools suffer from high overhead, with up to a 20 times slowdown, making them infeasible to be used for production-runs. This paper proposes a tool called SafeMem to detect memory leaks and memory corruption on-the-fly during production-runs. This tool does not rely on any new hardware support. Instead, it makes a novel use of existing ECC memory technology and exploits intelligent dynamic memory usage behavior analysis to detect memory leaks and corruption. We have evaluated SafeMem with seven real-world applications that contain memory leak or memory corruption bugs. SafeMem detects all tested bugs with low overhead (only 1.6 -14.4 ), 2-3 orders of magnitudes smaller than Purify. Our results also show that ECC-protection is effective in pruning false positives for memory leak detection, and in reducing the amount of memory waste (by a factor of 64-74) used for memory monitoring in memory corruption detection compared to page-protection.