Aggregator

A highly sophisticated phishing framework named Starkiller has recently emerged, offering attackers an advanced method to steal credentials and bypass multi-factor authentication. Developed by a group known as Jinkusu, this malicious toolkit is sold as a commercial software-as-a-service product. Unlike older toolkits relying on static copies of legitimate websites, this new platform loads real login […]

The post New Phishing Framework Starkiller Proxies Real Login Pages to Bypass MFA appeared first on Cyber Security News.

You must login to view this content

You must login to view this content

You must login to view this content

This blog post aims to explain what Claude Code Security is (recognizing few details are currently available), and how enterprises and developers should think about its role in their cybersecurity toolchain.

The post Thoughts on Claude Code Security appeared first on Security Boulevard.

Session 13B: API Security

Authors, Creators & Presenters: Jinghua Liu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Kai Chen (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Miaoqian Lin (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China)

PAPER
Generating API Parameter Security Rules with LLM for API Misuse Detection

When utilizing library APIs, developers should follow the API security rules to mitigate the risk of API misuse. API Parameter Security Rule (APSR) is a common type of security rule that specifies how API parameters should be safely used and places constraints on their values. Failure to comply with the APSRs can lead to severe security issues, including null pointer dereference and memory corruption. Manually analyzing numerous APIs and their parameters to construct APSRs is labor-intensive and needs to be automated. Existing studies generate APSRs from documentation and code, but the missing information and limited analysis heuristics result in missing APSRs. Due to the superior Large Language Model's (LLM) capability in code analysis and text generation without predefined heuristics, we attempt to utilize it to address the challenge encountered in API misuse detection. However, directly utilizing LLMs leads to incorrect APSRs which may lead to false bugs in detection, and overly general APSRs that could not generate applicable detection code resulting in many security bugs undiscovered. In this paper, we present a new framework, named GPTAid, for automatic APSRs generation by analyzing API source code with LLM and detecting API misuse caused by incorrect parameter use. To validate the correctness of the LLM-generated APSRs, we propose an execution feedback-checking approach based on the observation that security-critical API misuse is often caused by APSRs violations, and most of them result in runtime errors. Specifically, GPTAid first uses LLM to generate raw APSRs and the Right calling code, and then generates Violation code for each raw APSR by modifying the Right calling code using LLM. Subsequently, GPTAid performs dynamic execution on each piece of Violation code and further filters out the incorrect APSRs based on runtime errors. To further generate concrete APSRs, GPTAid employs a code differential analysis to refine the filtered ones. Particularly, as the programming language is more precise than natural language, GPTAid identifies the key operations within Violation code by differential analysis, and then generates the corresponding concrete APSR based on the aforementioned operations. These concrete APSRs could be precisely interpreted into applicable detection code, which proven to be effective in API misuse detection. Implementing on the dataset containing 200 randomly selected APIs from eight popular libraries, GPTAid achieves a precision of 92.3%. Moreover, it generates 6 times more APSRs than state-of-the-art detectors on a comparison dataset of previously reported bugs and APSRs. We further evaluated GPTAid on 47 applications, 210 unknown security bugs were found potentially resulting in severe security issues (e.g., system crashes), 150 of which have been confirmed by developers after our reports.

ABOUT NDSS
The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.

Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.

Permalink

The post NDSS 2025 – Generating API Parameter Security Rules With LLM For API Misuse Detection appeared first on Security Boulevard.

Apple chose Google's Gemini over ChatGPT for Siri's AI upgrade. This $1B/year deal reveals who's actually winning the AI race—and it's not who you think.

The post The Apple-Google AI Deal: What $1 Billion Says About Who’s Really Winning the AI Race appeared first on Security Boulevard.