The security landscape of software systems has witnessed considerable advancements through dynamic testing methodologies, especially fuzzing. Traditionally, fuzzing involves a sequential, cyclic process where software is tested to identify crashes. These crashes are then triaged and patched, leading to subsequent cycles that uncover further vulnerabilities. While effective, this method is not efficient as each cycle potentially reveals new issues previously obscured by earlier crashes, thus resulting in vulnerabilities being discovered sequentially.

The complex and diverse nature of software systems necessitates a careful manual approach to unveil vulnerabilities, involving deep analysis, creative problem-solving, and specialized expertise. Like all complex tasks, it’s susceptible to mistakes stemming from cognitive limitations and behavioral factors that hinder optimal performance. Although there are significant research efforts focused on vulnerability discovery, little attention has been given to comprehending mistakes within the process. Understanding these mistakes could pave the way for better-designed education programs and automated tools, aiming to mitigate and prevent potential mistakes and enhance the efficiency of vulnerability research.In this paper, we leverage social media, specifically YouTube, to examine mistakes made by security content creators exploiting vulnerabilities in CTF-style challenges. Analyzing 30 screencasts from 11 hackers, we identified 124 distinct issues and investigated their types, underlying causes, and time investments. Additionally, we delved into the cognitive and behavioral aspects associated with these issues.

We present a novel approach to automatically recover information about the address space layout of remote processes in the presence of Address Space Layout Randomization (ASLR). Our system, dubbed Sleak, performs static analysis and symbolic execution of binary executable programs, and identifies program paths and input parameters leading to partial (i.e., only a few bits) or complete (i.e., the whole address) information disclosure vulnerabilities, revealing addresses of known objects of the target service or application. Sleak takes, as input, the binary executable program, and generates a symbolic expression for each program output that leaks information about the addresses of objects, such as stack variables, heap structures, or function pointers. By comparing these expressions with the concrete output of a remote process executing the same binary program image, our system is able to recover from a few bits to whole addresses of objects of the target application or service. Discovering the address of a single object in the target application is often enough to guess the layout of entire sections of the address space, which can be leveraged by attackers to bypass ASLR.