Stories with Tag Security Testing

• Curl-impersonate: Special build of curl that can impersonate the major browsers

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  Posted: 2025-04-03 15:24:49

  Verbose tl;dr

  curl-impersonate is a specialized version of curl designed to mimic the behavior of popular web browsers like Chrome, Firefox, and Safari. It achieves this by accurately replicating their respective User-Agent strings, TLS fingerprints (including cipher suites and supported protocols), and HTTP header sets, making it a valuable tool for web developers and security researchers who need to test website compatibility and behavior across different browser environments. It simplifies the process of fetching web content as a specific browser would, allowing users to bypass browser-specific restrictions or analyze how a website responds to different browser profiles.

  curl-impersonate is a specialized version of the popular command-line tool curl, meticulously designed to mimic the network behavior of major web browsers like Chrome, Firefox, Safari, and Edge. This allows developers and security researchers to fetch web resources as if they were using these browsers, bypassing potential discrepancies in server responses that might arise from using a barebones tool like standard curl.

  The project achieves this impersonation by meticulously replicating crucial HTTP headers sent by these browsers, including the User-Agent, Accept, Accept-Language, and Accept-Encoding headers. These headers inform the server about the client's capabilities and preferences, influencing the type of content returned. For instance, a server might serve different content to a mobile browser compared to a desktop browser, and curl-impersonate allows you to test these variations easily.

  Furthermore, curl-impersonate goes beyond simply setting static header values. It offers the ability to emulate specific versions of these browsers, recognizing that header configurations change over time. This granular control ensures accurate simulation of a target browser's behavior for a particular release.

  The tool is built upon the standard curl utility, leveraging its core functionality while extending it with browser impersonation capabilities. This means users familiar with curl will find curl-impersonate easy to use, benefiting from the familiar command-line interface and options. It simplifies the process of testing website compatibility across different browsers and debugging issues related to browser-specific rendering or functionality without requiring actual browser instances.

  In essence, curl-impersonate provides a powerful and efficient way to inspect how a web server responds to requests from different browsers, facilitating tasks like web development, security testing, and web scraping by accurately simulating the browser environment from the command line. This enables users to identify potential issues stemming from browser incompatibility or server-side discrepancies and ensure consistent website behavior across different browsing platforms.

  • curl
  • web scraping
  • browser impersonation
  • User Agent
  • HTTP
  • HTTPS
  • TLS
  • Security Testing
  • Penetration Testing
  • website development
  • Debugging
  • command-line tool
  • Open Source
  • GitHub
  • Automation
  • web automation

  Summary of Comments ( 116 )
  https://news.ycombinator.com/item?id=43571099

  Verbose tl;dr

  Hacker News users discussed the practicality and potential misuse of curl-impersonate. Some praised its simplicity for testing and debugging, highlighting the ease of switching between browser profiles. Others expressed concern about its potential for abuse, particularly in fingerprinting and bypassing security measures. Several commenters questioned the long-term viability of the project given the rapid evolution of browser internals, suggesting that maintaining accurate impersonation would be challenging. The value for penetration testing was also debated, with some arguing its usefulness for identifying vulnerabilities while others pointed out its limitations in replicating complex browser behaviors. A few users mentioned alternative tools like mitmproxy offering more comprehensive browser manipulation.

  The Hacker News post titled "Curl-impersonate: Special build of curl that can impersonate the major browsers" (https://news.ycombinator.com/item?id=43571099) has generated a moderate number of comments discussing the project's utility, potential use cases, and some limitations.

  Several commenters express appreciation for the tool, finding it valuable for tasks like web scraping and testing. One user highlights its usefulness in bypassing bot detection mechanisms that rely on User-Agent strings, allowing them to access content otherwise blocked. Another user echoes this sentiment, specifically mentioning its application in interacting with websites that present different content based on the detected browser. A commenter points out the advantage of using a single, familiar tool like curl rather than needing to manage multiple browser installations or dedicated browser automation tools like Selenium for simple tasks.

  Some discussion revolves around the project's scope and functionality. One commenter questions whether it's genuinely "impersonating" browsers or simply changing the User-Agent string. Another clarifies that while the current implementation primarily focuses on User-Agent and TLS fingerprint modification, it's a step towards more comprehensive browser impersonation. This leads to a brief discussion about the complexities of truly mimicking browser behavior, including JavaScript execution and rendering engines, which are beyond the current scope of curl-impersonate.

  The project's reliance on pre-built binaries is also a topic of conversation. While some appreciate the ease of use provided by pre-built binaries, others express concern about the security implications of using binaries from an unknown source. The discussion touches upon the desire for build instructions to compile the tool from source for increased trust and platform compatibility. One user even suggests potential improvements like a Docker image to streamline the process and ensure a consistent environment.

  Finally, there's a brief exchange regarding the legal and ethical implications of using such a tool. One commenter cautions against using it for malicious purposes, highlighting the potential for bypassing security measures or impersonating users. Another user notes that using a custom User-Agent is generally acceptable as long as it's not used for deceptive practices.

  In summary, the comments generally portray curl-impersonate as a useful tool for specific web-related tasks. While acknowledging its limitations and potential for misuse, the overall sentiment leans towards appreciation for its simplicity and effectiveness in manipulating User-Agent strings and TLS fingerprints for legitimate purposes like testing and accessing differently rendered content. The comments also reflect a desire for more transparency and flexibility in terms of building the tool from source.

• Speedrunners are vulnerability researchers, they just don't know it yet

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  Posted: 2025-03-02 17:40:36

  Verbose tl;dr

  The blog post argues that speedrunners possess many of the same skills and mindsets as vulnerability researchers. They both meticulously analyze systems, searching for unusual behavior and edge cases that can be exploited for an advantage, whether that's saving milliseconds in a game or bypassing security measures. Speedrunners develop a deep understanding of a system's inner workings through experimentation and observation, often uncovering unintended functionality. This makes them naturally suited to vulnerability research, where finding and exploiting these hidden flaws is the primary goal. The author suggests that with some targeted training and a shift in focus, speedrunners could easily transition into security research, offering a fresh perspective and valuable skillset to the field.

  The blog post "Speedrunners are vulnerability researchers, they just don't know it yet," by Zetier, posits a compelling analogy between the activities of video game speedrunners and those engaged in vulnerability research within software and systems. The core argument revolves around the shared skillset and mindset both groups employ. Speedrunners, in their pursuit of minimizing playtime, meticulously analyze game mechanics, searching for unintended interactions and exploitable loopholes that allow them to bypass intended gameplay sequences. This process, the author argues, mirrors the work of vulnerability researchers, who similarly scrutinize software code and system architectures to uncover weaknesses and potential points of exploitation.

  The author elaborates on several key parallels. Firstly, both groups engage in deep, analytical dives into their respective targets. Speedrunners develop an intimate understanding of game logic, memory management, and even hardware quirks, while vulnerability researchers dissect code, network protocols, and system behavior. Secondly, both activities frequently involve manipulating inputs and observing the resulting outputs to identify anomalies and deviations from expected behavior. Speedrunners manipulate controller inputs, game states, and sometimes even hardware to trigger glitches and exploits, while vulnerability researchers craft specific input sequences, network packets, or data structures to probe for vulnerabilities.

  The post emphasizes the creative problem-solving inherent in both domains. Speedrunners often discover novel and unexpected ways to break games, employing out-of-the-box thinking to chain together seemingly unrelated glitches into powerful sequence breaks. Similarly, vulnerability researchers must think creatively to identify and exploit vulnerabilities that may be obscured by complex code or system design.

  Furthermore, both speedrunners and vulnerability researchers benefit from a collaborative community. Speedrunners share their findings, techniques, and strategies through online forums, videos, and live streams, accelerating the discovery and refinement of new exploits. Analogously, the vulnerability research community shares information through responsible disclosure platforms, conferences, and publications, contributing to a collective understanding of software security.

  The author concludes by suggesting that the skills honed by speedrunners are highly transferable to the field of vulnerability research. The ability to meticulously analyze complex systems, identify and exploit unintended behavior, and think creatively to solve complex problems are valuable assets in both domains. The post implies that recognizing and fostering this connection could be beneficial to the cybersecurity industry, potentially tapping into a large pool of individuals with the aptitude and passion for uncovering and understanding vulnerabilities. The underlying message encourages individuals with a passion for speedrunning to consider applying their skills to the field of cybersecurity.

  • Speedrunning
  • Vulnerability Research
  • Software Security
  • game exploitation
  • Glitches
  • Reverse Engineering
  • Debugging
  • software testing
  • unintended behavior
  • Game Design
  • Security Testing
  • QA testing

  Summary of Comments ( 57 )
  https://news.ycombinator.com/item?id=43232880

  Verbose tl;dr

  HN commenters largely agree with the premise that speedrunners possess skills applicable to vulnerability research. Several highlighted the meticulous understanding of game mechanics and the ability to manipulate code execution paths as key overlaps. One commenter mentioned the "arbitrary code execution" goal of both speedrunners and security researchers, while another emphasized the creative problem-solving mindset required for both disciplines. A few pointed out that speedrunners already perform a form of vulnerability research when discovering glitches and exploits. Some suggested that formalizing a pathway for speedrunners to transition into security research would be beneficial. The potential for identifying vulnerabilities before game release through speedrunning techniques was also raised.

  The Hacker News post titled "Speedrunners are vulnerability researchers, they just don't know it yet" sparked a lively discussion with several compelling comments.

  Many commenters agreed with the premise, highlighting the similarities between speedrunning techniques and vulnerability research. One commenter pointed out that speedrunners, like security researchers, deeply understand the systems they're working with, often finding unintended behaviors and exploiting edge cases. They emphasized that both groups rely on meticulous documentation and sharing of findings within their communities.

  Another commenter drew a parallel between sequence breaking in speedrunning and exploiting vulnerabilities in software. They explained how both involve understanding the underlying logic of a system to manipulate it in unexpected ways. This commenter also highlighted the iterative nature of both activities, where small optimizations accumulate to create significant overall improvements.

  Some comments focused on the potential benefits of recruiting speedrunners for security research roles. One commenter suggested that speedrunners possess a natural curiosity and persistence that would be valuable in this field. They also noted that the competitive nature of speedrunning could translate well to the challenge-driven world of vulnerability research.

  A few commenters offered counterpoints, acknowledging the overlap between the two fields but also highlighting key differences. They argued that while speedrunners exploit unintended behavior within the defined rules of a game, security researchers often deal with malicious actors exploiting vulnerabilities outside of any intended use case. This difference in context and motivation, they argued, necessitates a distinct skillset despite the shared analytical approach.

  Another dissenting comment emphasized the difference in scope. While speedrunners focus on optimizing for speed within a known and controlled environment, security researchers often have to deal with complex and evolving systems where the full extent of vulnerabilities might be unknown.

  One commenter provided a personal anecdote about a friend who transitioned from speedrunning to a career in security, further reinforcing the connection between the two fields. This story offered a practical example of how the skills honed through speedrunning can be directly applicable to security research.

  Several commenters also discussed the legal and ethical implications of exploiting vulnerabilities, drawing a distinction between the acceptable practice within the controlled environment of a game versus the potential harm caused by exploiting vulnerabilities in real-world software systems.

  Overall, the discussion on Hacker News affirmed the core argument that speedrunners possess skills and traits valuable to vulnerability research. While some commenters nuanced the comparison and highlighted key differences, the general consensus was that the mindset and methodologies employed by speedrunners have significant overlap with those used in security research.

• Garak, LLM Vulnerability Scanner

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  Posted: 2024-11-17 11:37:45

  Verbose tl;dr

  Garak is an open-source tool developed by NVIDIA for identifying vulnerabilities in large language models (LLMs). It probes LLMs with a diverse range of prompts designed to elicit problematic behaviors, such as generating harmful content, leaking private information, or being easily jailbroken. These prompts cover various attack categories like prompt injection, data poisoning, and bias detection. Garak aims to help developers understand and mitigate these risks, ultimately making LLMs safer and more robust. It provides a framework for automated testing and evaluation, allowing researchers and developers to proactively assess LLM security and identify potential weaknesses before deployment.

  NVIDIA has introduced Garak, a novel open-source tool specifically designed to rigorously assess the security vulnerabilities of Large Language Models (LLMs). Garak operates by systematically generating a diverse and extensive array of adversarial prompts, meticulously crafted to exploit potential weaknesses within these models. These prompts are then fed into the target LLM, and the resulting output is meticulously analyzed for a range of problematic behaviors.

  Garak's focus extends beyond simple prompt injection attacks. It aims to uncover a broad spectrum of vulnerabilities, including but not limited to jailbreaking (circumventing safety guidelines), prompt leaking (inadvertently revealing sensitive information from the training data), and generating biased or harmful content. The tool facilitates a deeper understanding of the security landscape of LLMs by providing researchers and developers with a robust framework for identifying and mitigating these risks.

  Garak's architecture emphasizes flexibility and extensibility. It employs a modular design that allows users to easily integrate custom prompt generation strategies, vulnerability detectors, and output analyzers. This modularity allows researchers to tailor Garak to their specific needs and investigate specific types of vulnerabilities. The tool also incorporates various pre-built modules and templates, providing a readily available starting point for evaluating LLMs. This includes a collection of known adversarial prompts and detectors for common vulnerabilities, simplifying the initial setup and usage of the tool.

  Furthermore, Garak offers robust reporting capabilities, providing detailed logs and summaries of the testing process. This documentation helps in understanding the identified vulnerabilities, the prompts that triggered them, and the LLM's responses. This comprehensive reporting aids in the analysis and interpretation of the test results, enabling more effective remediation efforts. By offering a systematic and thorough approach to LLM vulnerability scanning, Garak empowers developers to build more secure and robust language models. It represents a significant step towards strengthening the security posture of LLMs in the face of increasingly sophisticated adversarial attacks.

  • LLM
  • Large Language Model
  • Vulnerability Scanner
  • Security
  • AI Safety
  • AI Security
  • Red Teaming
  • Prompt Injection
  • Jailbreaking
  • Security Testing
  • Nvidia
  • Garak
  • Open Source
  • Tooling
  • machine learning
  • artificial intelligence
  • Jailbreak
  • Adversarial Attacks
  • Security Tool
  • Code Security
  • LLM Security
  • Prompt Engineering
  • Cybersecurity
  • AI Red Teaming
  • LLM Attack
  • LLM Defense
  • Python
  • Framework
  • fuzzing
  • Robustness

  Summary of Comments ( 62 )
  https://news.ycombinator.com/item?id=42163591

  Verbose tl;dr

  Hacker News commenters discuss Garak's potential usefulness while acknowledging its limitations. Some express skepticism about the effectiveness of LLMs scanning other LLMs for vulnerabilities, citing the inherent difficulty in defining and detecting such issues. Others see value in Garak as a tool for identifying potential problems, especially in specific domains like prompt injection. The limited scope of the current version is noted, with users hoping for future expansion to cover more vulnerabilities and models. Several commenters highlight the rapid pace of development in this space, suggesting Garak represents an early but important step towards more robust LLM security. The "arms race" analogy between developing secure LLMs and finding vulnerabilities is also mentioned.

  The Hacker News post for "Garak, LLM Vulnerability Scanner" sparked a fairly active discussion with a variety of viewpoints on the tool and its implications.

  Several commenters expressed skepticism about the practical usefulness of Garak, particularly in its current early stage. One commenter questioned whether the provided examples of vulnerabilities were truly exploitable, suggesting they were more akin to "jailbreaks" that rely on clever prompting rather than representing genuine security risks. They argued that focusing on such prompts distracts from real vulnerabilities, like data leakage or biased outputs. This sentiment was echoed by another commenter who emphasized that the primary concern with LLMs isn't malicious code execution but rather undesirable outputs like harmful content. They suggested current efforts are akin to "penetration testing a calculator" and miss the larger point of LLM safety.

  Others discussed the broader context of LLM security. One commenter highlighted the challenge of defining "vulnerability" in the context of LLMs, as it differs significantly from traditional software. They suggested the focus should be on aligning LLM behavior with human values and intentions, rather than solely on preventing specific prompt injections. Another discussion thread explored the analogy between LLMs and social engineering, with one commenter arguing that LLMs are inherently susceptible to manipulation due to their reliance on statistical patterns, making robust defense against prompt injection difficult.

  Some commenters focused on the technical aspects of Garak and LLM vulnerabilities. One suggested incorporating techniques from fuzzing and symbolic execution to improve the tool's ability to discover vulnerabilities. Another discussed the difficulty of distinguishing between genuine vulnerabilities and intentional features, using the example of asking an LLM to generate offensive content.

  There was also some discussion about the potential misuse of tools like Garak. One commenter expressed concern that publicly releasing such a tool could enable malicious actors to exploit LLMs more easily. Another countered this by arguing that open-sourcing security tools allows for faster identification and patching of vulnerabilities.

  Finally, a few commenters offered more practical suggestions. One suggested using Garak to create a "robustness score" for LLMs, which could help users choose models that are less susceptible to manipulation. Another pointed out the potential use of Garak in red teaming exercises.

  In summary, the comments reflected a wide range of opinions and perspectives on Garak and LLM security, from skepticism about the tool's practical value to discussions of broader ethical and technical challenges. The most compelling comments highlighted the difficulty of defining and addressing LLM vulnerabilities, the need for a shift in focus from prompt injection to broader alignment concerns, and the potential benefits and risks of open-sourcing LLM security tools.

• Fuzzing the PHP Interpreter via Dataflow Fusion

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  Posted: 2024-11-15 15:36:53

  Verbose tl;dr

  This paper introduces a new fuzzing technique called Dataflow Fusion (DFusion) specifically designed for complex interpreters like PHP. DFusion addresses the challenge of efficiently exploring deep execution paths within interpreters by strategically combining coverage-guided fuzzing with taint analysis. It identifies critical dataflow paths and generates inputs that maximize the exploration of these paths, leading to the discovery of more bugs. The researchers evaluated DFusion against existing PHP fuzzers and demonstrated its effectiveness in uncovering previously unknown vulnerabilities, including crashes and memory safety issues, within the PHP interpreter. Their results highlight the potential of DFusion for improving the security and reliability of interpreted languages.

  The research paper "Fuzzing the PHP Interpreter via Dataflow Fusion" introduces a novel fuzzing technique specifically designed for complex interpreters like PHP. The authors argue that existing fuzzing methods often struggle with these interpreters due to their intricate internal structures and dynamic behaviors. They propose a new approach called Dataflow Fusion, which aims to enhance the effectiveness of fuzzing by strategically combining different dataflow analysis techniques.

  Traditional fuzzing relies heavily on code coverage, attempting to explore as many different execution paths as possible. However, in complex interpreters, achieving high coverage can be challenging and doesn't necessarily correlate with uncovering deep bugs. Dataflow Fusion tackles this limitation by moving beyond simple code coverage and focusing on the flow of data within the interpreter.

  The core idea behind Dataflow Fusion is to leverage multiple dataflow analyses, specifically taint analysis and control-flow analysis, and fuse their results to guide the fuzzing process more intelligently. Taint analysis tracks the propagation of user-supplied input through the interpreter, identifying potential vulnerabilities where untrusted data influences critical operations. Control-flow analysis, on the other hand, maps out the possible execution paths within the interpreter. By combining these two analyses, Dataflow Fusion can identify specific areas of the interpreter's code where tainted data affects control flow, thus pinpointing potentially vulnerable locations.

  The paper details the implementation of Dataflow Fusion within a custom fuzzer for the PHP interpreter. This fuzzer uses a hybrid approach, combining both mutation-based fuzzing, which modifies existing inputs, and generation-based fuzzing, which creates entirely new inputs. The fuzzer is guided by the Dataflow Fusion engine, which prioritizes inputs that are likely to explore interesting and potentially vulnerable paths within the interpreter.

  The authors evaluate the effectiveness of their approach by comparing it to existing fuzzing techniques. Their experiments demonstrate that Dataflow Fusion significantly outperforms traditional fuzzing methods in terms of bug discovery. They report uncovering a number of previously unknown vulnerabilities in the PHP interpreter, including several critical security flaws. These findings highlight the potential of Dataflow Fusion to improve the security of complex interpreters.

  Furthermore, the paper discusses the challenges and limitations of the proposed approach. Dataflow analysis can be computationally expensive, particularly for large and complex interpreters. The authors address this issue by employing various optimization techniques to improve the performance of the Dataflow Fusion engine. They also acknowledge that Dataflow Fusion, like any fuzzing technique, is not a silver bullet and may not be able to uncover all vulnerabilities. However, their results suggest that it represents a significant step forward in the ongoing effort to improve the security of complex software systems. The paper concludes by suggesting future research directions, including exploring the applicability of Dataflow Fusion to other interpreters and programming languages.

  • fuzzing
  • php
  • interpreter
  • dataflow analysis
  • data flow fusion
  • Security Testing
  • software testing
  • vulnerability discovery
  • program analysis
  • compiler optimization
  • dynamic analysis
  • dataflow fusion
  • Security

  Summary of Comments ( 3 )
  https://news.ycombinator.com/item?id=42147833

  Verbose tl;dr

  Hacker News users discussed the potential impact and novelty of the PHP fuzzer described in the linked paper. Several commenters expressed skepticism about the significance of the discovered vulnerabilities, pointing out that many seemed related to edge cases or functionalities rarely used in real-world PHP applications. Others questioned the fuzzer's ability to uncover truly impactful bugs compared to existing methods. Some discussion revolved around the technical details of the fuzzing technique, "dataflow fusion," with users inquiring about its specific advantages and limitations. There was also debate about the general state of PHP security and whether this research represents a meaningful advancement in securing the language.

  The Hacker News post titled "Fuzzing the PHP Interpreter via Dataflow Fusion" (https://news.ycombinator.com/item?id=42147833) has several comments discussing the linked research paper. The discussion revolves around the effectiveness and novelty of the presented fuzzing technique.

  One commenter highlights the impressive nature of finding 189 unique bugs, especially considering PHP's maturity and the extensive testing it already undergoes. They point out the difficulty of fuzzing interpreters in general and praise the researchers' approach.

  Another commenter questions the significance of the found bugs, wondering how many are exploitable and pose a real security risk. They acknowledge the value of finding any bugs but emphasize the importance of distinguishing between minor issues and serious vulnerabilities. This comment sparks a discussion about the nature of fuzzing, with replies explaining that fuzzing often reveals unexpected edge cases and vulnerabilities that traditional testing might miss. It's also mentioned that while not all bugs found through fuzzing are immediately exploitable, they can still provide valuable insights into potential weaknesses and contribute to the overall robustness of the software.

  The discussion also touches on the technical details of the "dataflow fusion" technique used in the research. One commenter asks for clarification on how this approach differs from traditional fuzzing methods, prompting a response explaining the innovative aspects of combining dataflow analysis with fuzzing. This fusion allows for more targeted and efficient exploration of the interpreter's state space, leading to a higher likelihood of uncovering bugs.

  Furthermore, a commenter with experience in PHP internals shares insights into the challenges of maintaining and debugging such a complex codebase. They appreciate the research for contributing to the improvement of PHP's stability and security.

  Finally, there's a brief exchange about the practical implications of these findings, with commenters speculating about potential patches and updates to the PHP interpreter based on the discovered vulnerabilities.

  Overall, the comments reflect a positive reception of the research, acknowledging the challenges of fuzzing interpreters and praising the researchers' innovative approach and the significant number of bugs discovered. There's also a healthy discussion about the practical implications of the findings and the importance of distinguishing between minor bugs and serious security vulnerabilities.