Memory-Safety Challenge Considered Solved? An In-Depth Study with All Rust CVEs

Rust is an emerging programming language that aims at preventing memory-safety bugs without sacrificing much efficiency. The claimed property is very attractive to developers, and many projects start using the language. However, can Rust achieve the memory-safety promise? This article studies the question by surveying 186 real-world bug reports collected from several origins, which contain all existing Rust common vulnerability and exposures (CVEs) of memory-safety issues by 2020-12-31. We manually analyze each bug and extract their culprit patterns. Our analysis result shows that Rust can keep its promise that all memory-safety bugs require unsafe code, and many memory-safety bugs in our dataset are mild soundness issues that only leave a possibility to write memory-safety bugs without unsafe code. Furthermore, we summarize three typical categories of memory-safety bugs, including automatic memory reclaim, unsound function, and unsound generic or trait. While automatic memory claim bugs are related to the side effect of Rust newly-adopted ownership-based resource management scheme, unsound function reveals the essential challenge of Rust development for avoiding unsound code, and unsound generic or trait intensifies the risk of introducing unsoundness. Based on these findings, we propose two promising directions toward improving the security of Rust development, including several best practices of using specific APIs and methods to detect particular bugs involving unsafe code. Our work intends to raise more discussions regarding the memory-safety issues of Rust and facilitate the maturity of the language.

Persistent software transactional memory in Haskell

Emerging persistent memory in commodity hardware allows byte-granular accesses to persistent state at memory speeds. However, to prevent inconsistent state in persistent memory due to unexpected system failures, different write-semantics are required compared to volatile memory. Transaction-based library solutions for persistent memory facilitate the atomic modification of persistent data in languages where memory is explicitly managed by the programmer, such as C/C++. For languages that provide extended capabilities like automatic memory management, a more native integration into the language is needed to maintain the high level of memory abstraction. It is shown in this paper how persistent software transactional memory (PSTM) can be tightly integrated into the runtime system of Haskell to atomically manage values of persistent transactional data types. PSTM has a clear interface and semantics extending that of software transactional memory (STM). Its integration with the language’s memory management retains features like garbage collection and allocation strategies, and is fully compatible with Haskell's lazy execution model. Our PSTM implementation demonstrates competitive performance with low level libraries and trivial portability of existing STM libraries to PSTM. The implementation allows further interesting use cases, such as persistent memoization and persistent Haskell expressions.

Reflex memory theory of acquired involuntary motor and sensory disorders

Background Explicit and implicit memories are conserved but flexible biological tools that nature uses to regulate the daily behaviors of human beings. An aberrant form of the implicit memory is presumed to exist and may be contributory to the pathophysiology of disorders such as tardive syndromes, phantom phenomena, flashback, posttraumatic stress disorders (PTSD), and related disorders. These disorders have posed significant clinical problems for both patients and physicians for centuries. All extant pathophysiological theories of these disorders have failed to provide basis for effective treatment. Objective The objective of this article is to propose an alternative pathophysiological theory that will hopefully lead to new treatment approaches. Methods The author sourced over 60 journal articles that treated topics on memory, and involuntary motor and sensory disorders, from open access journals using Google Scholar, and reviewed them and this helped in the formulation of this theory. Results From the reviews, the author thinks physical or chemical insult to the nervous system can cause defective circuit remodeling, leading to generation of a variant of implicit (automatic) memory, herein called “reflex memory” and this is encoded interoceptively to contribute to these phenomena states. Conclusion Acquired involuntary motor and sensory disorders are caused by defective circuit remodeling involving multiple neural mechanisms. Dysregulation of excitatory neurotransmitters, calcium overload, homeostatic failure, and neurotoxicity are implicated in the process. Sustained effects of these defective mechanisms are encoded interoceptively as abnormal memory in the neurons and the conscious manifestations are these disorders. Extant theories failed to recognize this possibility.

How to Remember Something You Didn't Say

When individuals suppress secret information, they should keep this omission in mind to not let this information slip out in future situations. Following recent findings about automatic memory retrieval of outright lies, we hypothesized that suppression tendencies are also automatically retrieved from memory when being confronted with a question to which one has previously omitted secret information. In an online study, participants first had to withhold information about a fictitious love affair during a simulated chat with their relationship partner. To assess automatic suppression tendencies, we developed an indirect response time measure wherein a key that had previously been established to indicate suppression now had to be pressed in response to word stimuli that were presented in a specific color. We found implicit suppression tendencies for words that had been withheld during the interview if they were presented following the prime that involved the question which the secret answer referred to. The question primes or the secret information alone did not elicit a suppression tendency, indicating that suppression responses were automatically retrieved from memory after re-encountering the combination of the question and the critical answer. The results are discussed regarding the theoretical implications for automatic memory processes.

Quantity matters: The frequency of deception influences automatic memory retrieval effects

We investigated automatic retrieval of the knowledge of having lied or having told the truth to a question, depending on (a) the quality of the statement (true vs. false response) and (b) the overall proportion of (dis-)honest responses. We therefore manipulated the proportion of lies and truths being told in an oral interview. Automatic retrieval of this meta-knowledge was assessed with a categorisation task, where the probe words dishonest and honest had to be classified, while questions from the interview served as task-irrelevant prime stimuli. Results revealed an automatic retrieval of knowledge about having lied to a question only for participants who had told few lies in the interview, but not for those who had told many lies. No retrieval effects were obtained regarding questions that had been answered truthfully. These findings suggest a combined influence of quality and quantity of dishonest statements on automatic memory retrieval, thereby being in accordance with recent accounts of action control.

A performance comparison of garbage collector algorithms in Java Virtual Machine

In programming languages with automatic memory management garbage collection plays an important role of cleaning unused memory. Garbage collection algorithms have been developed for many years and aim to maximize the application’s performance. This paper presents and compares a performance of five garbage collection algorithms present in current version of Java 12 in three applications with different object lifetime span. The analysis covered the system responsiveness, garbage collector workload and application throughput at high application load.

ARMIN: Towards a More Efficient and Light-weight Recurrent Memory Network

In recent years, memory-augmented neural networks(MANNs) have shown promising power to enhance the memory ability of neural networks for sequential processing tasks. However, previous MANNs suffer from complex memory addressing mechanism, making them relatively hard to train and causing computational overheads. Moreover, many of them reuse the classical RNN structure such as LSTM for memory processing, causing inefficient exploitations of memory information. In this paper, we introduce a novel MANN, the Auto-addressing and Recurrent Memory Integrating Network (ARMIN) to address these issues. The ARMIN only utilizes hidden state h_t for automatic memory addressing, and uses a novel RNN cell for refined integration of memory information. Empirical results on a variety of experiments demonstrate that the ARMIN is more light-weight and efficient compared to existing memory networks. Moreover, we demonstrate that the ARMIN can achieve much lower computational overhead than vanilla LSTM while keeping similar performances. Codes are available on github.com/zoharli/armin.