Conventional Knowledge Graph Reasoning (KGR) models learn the embeddings of KG components over the structure of KGs, but their performances are limited when the KGs are severely incomplete.Recent LLM-enhanced KGR models input KG structural information into LLMs.However, they require fine-tuning on open-source LLMs and are not applicable to closed-source LLMs.Therefore, in this paper, to leverage the knowledge in LLMs without fine-tuning to assist and enhance conventional KGR models, we propose a new three-stage pipeline, including knowledge alignment, KG reasoning and entity reranking.Specifically, in the alignment stage, we propose three strategies to align the knowledge in LLMs to the KG schema by explicitly associating unconnected nodes with semantic relations.Based on the enriched KGs, we train structure-aware KGR models to integrate aligned knowledge to original knowledge existing in KGs.In the reranking stage, after obtaining the results of KGR models, we rerank the top-scored entities with LLMs to recall correct answers further.Experiments show our pipeline can enhance the KGR performance in both incomplete and general situations.

Feature-rich software programs typically provide many configuration options for users to enable and disable features, or tune feature behaviors. Given the values of configuration options, certain code blocks in a program will become redundant code and never be used. However, the redundant code is still present in the program and thus unnecessarily increases a program's attack surface by allowing attackers to use it as return-oriented programming (ROP) gadgets. Existing code debloating techniques have several limitations: not targeting this type of redundant code, requiring access to program source code or user-provided test inputs. In this paper, we propose a practical code debloating approach, called BinDebloat, to address these limitations. BinDebloat identifies and removes redundant code caused by configuration option values. It does not require user-provided test inputs, or support from program developers, and is designed to work on closed-source programs. It uses static program analysis to identify code blocks that are control-dependent on configuration option values. Given a set of configuration option values, it automatically determines which of such code blocks become redundant and uses static binary rewriting to neutralize these code blocks so that they are removed from the attack surface. We evaluated BinDebloat on closed-source Windows programs and the results show that BinDebloat can effectively reduce a program's attack surface.

Detecting integer overflow vulnerabilities is critical for software security. Many techniques have been proposed to dynamically detect integer overflow vulnerabilities by instrumenting integer overflow tests into target programs. Their major drawback is that they can produce many false positives. In this paper, we propose an approach to eliminate the false positives stemming from incorrectly or not considering the sanitization code in target programs that is designed by developers to catch integer overflows.

Security vulnerabilities are among the most critical software defects in existence. When identified, programmers aim to produce patches that prevent the vulnerability as quickly as possible, motivating the need for automatic program repair (APR) methods to generate patches automatically. Unfortunately, most current APR methods fall short because they approximate the properties necessary to prevent the vulnerability using examples. Approximations result in patches that either do not fix the vulnerability comprehensively, or may even introduce new bugs. Instead, we propose property-based APR, which uses human-specified, program-independent and vulnerability-specific safety properties to derive source code patches for security vulnerabilities. Unlike properties that are approximated by observing the execution of test cases, such safety properties are precise and complete. The primary challenge lies in mapping such safety properties into source code patches that can be instantiated into an existing program. To address these challenges, we propose Senx, which, given a set of safety properties and a single input that triggers the vulnerability, detects the safety property violated by the vulnerability input and generates a corresponding patch that enforces the safety property and thus, removes the vulnerability. Senx solves several challenges with property-based APR: it identifies the program expressions and variables that must be evaluated to check safety properties and identifies the program scopes where they can be evaluated, it generates new code to selectively compute the values it needs if calling existing program code would cause unwanted side effects, and it uses a novel access range analysis technique to avoid placing patches inside loops where it could incur performance overhead. Our evaluation shows that the patches generated by Senx successfully fix 32 of 42 real-world vulnerabilities from 11 applications including various tools or libraries for manipulating graphics/media files, a programming language interpreter, a relational database engine, a collection of programming tools for creating and managing binary programs, and a collection of basic file, shell, and text manipulation tools.

There is often a considerable delay between the discovery of a vulnerability and the issue of a patch. One way to mitigate this window of vulnerability is to use a configuration workaround, which prevents the vulnerable code from being executed at the cost of some lost functionality -- but only if one is available. Since application configurations are not specifically designed to mitigate software vulnerabilities, we find that they only cover 25.2% of vulnerabilities. To minimize patch delay vulnerabilities and address the limitations of configuration workarounds, we propose Security Workarounds for Rapid Response (SWRRs), which are designed to neutralize security vulnerabilities in a timely, secure, and unobtrusive manner. Similar to configuration workarounds, SWRRs neutralize vulnerabilities by preventing vulnerable code from being executed at the cost of some lost functionality. However, the key difference is that SWRRs use existing error-handling code within applications, which enables them to be mechanically inserted with minimal knowledge of the application and minimal developer effort. This allows SWRRs to achieve high coverage while still being fast and easy to deploy. We have designed and implemented Talos, a system that mechanically instruments SWRRs into a given application, and evaluate it on five popular Linux server applications. We run exploits against 11 real-world software vulnerabilities and show that SWRRs neutralize the vulnerabilities in all cases. Quantitative measurements on 320 SWRRs indicate that SWRRs instrumented by Talos can neutralize 75.1% of all potential vulnerabilities and incur a loss of functionality similar to configuration workarounds in 71.3% of those cases. Our overall conclusion is that automatically generated SWRRs can safely mitigate 2.1× more vulnerabilities, while only incurring a loss of functionality comparable to that of traditional configuration workarounds.

Effective machine-aided diagnosis and repair of configuration errors continues to elude computer systems designers. Most of the literature targets errors that can be attributed to a single erroneous configuration setting. However, a recent study found that a significant amount of configuration errors require fixing more than one setting together. To address this limitation, Ocasta statistically clusters dependent configuration settings based on the application's accesses to its configuration settings and utilizes the extracted clustering of configuration settings to fix configuration errors involving more than one configuration settings. Ocasta treats applications as black-boxes and only relies on the ability to observe application accesses to their configuration settings. We collected traces of real application usage from 24 Linux and 5 Windows desktops computers and found that Ocasta is able to correctly identify clusters with 88.6% accuracy. To demonstrate the effectiveness of Ocasta, we evaluated it on 16 real-world configuration errors of 11 Linux and Windows applications. Ocasta is able to successfully repair all evaluated configuration errors in 11 minutes on average and only requires the user to examine an average of 3 screenshots of the output of the application to confirm that the error is repaired. A user study we conducted shows that Ocasta is easy to use by both expert and non-expert users and is more efficient than manual configuration error troubleshooting.

Modern smartphone operating systems (OSs) have been developed with a greater emphasis on security and protecting privacy. One of the mechanisms these systems use to protect users is a permission system, which requires developers to declare what sensitive resources their applications will use, has users agree with this request when they install the application and constrains the application to the requested resources during runtime. As these permission systems become more common, questions have risen about their design and implementation. In this paper, we perform an analysis of the permission system of the Android smartphone OS in an attempt to begin answering some of these questions. Because the documentation of Android's permission system is incomplete and because we wanted to be able to analyze several versions of Android, we developed PScout, a tool that extracts the permission specification from the Android OS source code using static analysis. PScout overcomes several challenges, such as scalability due to Android's 3.4 million line code base, accounting for permission enforcement across processes due to Android's use of IPC, and abstracting Android's diverse permission checking mechanisms into a single primitive for analysis.