Robert Biddle

Tianhe Lu, Eric Spero, Sakuna Harinda Jayasundara et al.

The misuse of Java security APIs is a serious security problem in software development. Research in 2024 has shown that this problem is widespread in LLM-generated code. However, it remains unclear whether this phenomenon persists in current models and how external security knowledge affects it. This paper presents a scoped replication and extension of Mousavi et al.'s study on the Java Cryptography Architecture (JCA) and Java Secure Socket Extension (JSSE) APIs. We focus on two complementary settings: GPT-5.5 as a frontier proprietary coding model, and Llama-3.3-70B-Instruct as a strong open-weight model relevant to self-hosted deployment. The results show that although newer LLMs perform better in using Java security APIs, the problem of Java security API misuse has not been eliminated. External security knowledge substantially improves the measured outcome, but its effect is model-dependent. For Llama-3.3-70B-Instruct, secure code examples are the most effective single knowledge type. For GPT-5.5, explicit misuse patterns eliminate all detected security API misuses among valid programs in our benchmark, although some outputs remain invalid due to compilation errors or target-API mismatches. In addition, developer-guide knowledge becomes much more effective, and secure prompting also provides large gains for GPT-5.5. Overall, these findings confirm the Java security API misuse risk identified in the original study and show that the benefits of retrieval-augmented knowledge depend not only on the knowledge itself and retrieval behavior, but also on model capability.

Sijie Zhuo, Robert Biddle, Yun Sing Koh et al.

Phishing is recognised as a serious threat to organisations and individuals. While there have been significant technical advances in blocking phishing attacks, people remain the last line of defence after phishing emails reach their email client. Most of the existing literature on this subject has focused on the technical aspects related to phishing. However, the factors that cause humans to be susceptible to phishing attacks are still not well-understood. To fill this gap, we reviewed the available literature and we propose a three-stage Phishing Susceptibility Model (PSM) for explaining how humans are involved in phishing detection and prevention, and we systematically investigate the phishing susceptibility variables studied in the literature and taxonomize them using our model. This model reveals several research gaps that need to be addressed to improve users' detection performance. We also propose a practical impact assessment of the value of studying the phishing susceptibility variables, and quality of evidence criteria. These can serve as guidelines for future research to improve experiment design, result quality, and increase the reliability and generalizability of findings.