Large Language Models (LLMs) are gaining momentum in software development with prompt-driven programming enabling developers to create code from Natural Language (NL) instructions. However, studies have questioned their ability to produce secure code and, thereby, the quality of prompt-generated software. Alongside, various prompting techniques that carefully tailor prompts have emerged to elicit optimal responses from LLMs. Still, the interplay between such prompting strategies and secure code generation remains under-explored and calls for further investigations. Objective : In this study, we investigate the impact of different prompting techniques on the security of code generated from NL instructions by LLMs. Method : First, we perform a systematic literature review to identify the existing prompting techniques that can be used for code generation tasks. A subset of these techniques are evaluated on GPT-3, GPT-3.5, and GPT-4 models for secure code generation. For this, we used an existing dataset consisting of 150 NL security-relevant code generation prompts. Results : Our work (i) classifies potential prompting techniques for code generation (ii) adapts and evaluates a subset of the identified techniques for secure code generation tasks, and (iii) observes a reduction in security weaknesses across the tested LLMs, especially after using an existing technique called Recursive Criticism and Improvement (RCI), contributing valuable insights to the ongoing discourse on LLM-generated code security.

Cloud-based application deployment is becoming increasingly popular among businesses, thanks to the emergence of microservices. However, securing such architectures is a challenging task since traditional security concepts cannot be directly applied to microservice architectures due to their distributed nature. The situation is exacerbated by the scattered nature of guidelines and best practices advocated by practitioners and organizations in this field. In this research paper we aim to shay light over the current microservice security discussions hidden within Grey Literature (GL) sources. Particularly, we identify the challenges that arise when securing microservice architectures, as well as solutions recommended by practitioners to address these issues. For this, we conducted a systematic GL study on the challenges and best practices of microservice security present in the Internet with the goal of capturing relevant discussions in blogs, white papers, and standards. We collected 312 GL sources from which 57 were rigorously classified and analyzed. This analysis on the one hand validated past academic literature studies in the area of microservice security, but it also identified improvements to existing methodologies pointing towards future research directions.

In the real world, the disclosure of private information to others often occurs after a trustworthy relationship has been established. Conversely, users of Social Network Sites (SNSs) like Facebook or Instagram often disclose large amounts of personal information prematurely to individuals which are not necessarily trustworthy. Such a low privacy-preserving behavior is often exploited by deceptive attackers with harmful intentions. Basically, deceivers approach their victims in online communities using incentives that motivate them to share their private information, and ultimately, their credentials. Since motivations, such as financial or social gain vary from individual to individual, deceivers must wisely choose their incentive strategy to mislead the users. Consequently, attacks are crafted to each victim based on their particular information-sharing motivations. This work analyses, through an online survey, those motivations and cognitive biases which are frequently exploited by deceptive attackers in SNSs. We propose thereafter some countermeasures for each of these biases to provide personalized privacy protection against deceivers.

Like in the real world, perceptions of risk can influence the behavior and decisions that people make in online platforms. Users of Social Network Sites (SNSs) like Facebook make continuous decisions about their privacy since these are spaces designed to share private information with large and diverse audiences. In particular, deciding whether or not to disclose such information will depend largely on each individual’s ability to assess the corresponding privacy risks. However, SNSs often lack awareness instruments that inform users about the consequences of unrestrained self-disclosure practices. Such an absence of risk information can lead to poor assessments and, consequently, undermine users’ privacy behavior. This work elaborates on the use of risk scenarios as a strategy for promoting safer privacy decisions in SNSs. In particular, we investigate, through an online survey, the effects of communicating those risks associated with online self-disclosure. Furthermore, we analyze the users’ perceived severity of privacy threats and its importance for the definition of personalized risk awareness mechanisms. Based on our findings, we introduce the design of preventative nudges as an approach for providing individual privacy support and guidance in SNSs.

The microservice architecture presents many challenges from a security perspective, due to the large amount of services, leading to an increased attack surface and an unmanageble cognitive load for security analysts. Several benchmarks exist to guide the secure configuration of the deployment infrastructure for microservice applications, including containers (e.g., Docker), orchestration systems (e.g., Kubernetes), cloud platforms (e.g., AWS), and even operating systems (e.g., Linux). In this paper we approach the creation of a benchmark for the design of the microservice applications themselves. To this aim, we inventorize a number of relevant security rules for the architectural design of microservice applications and assess (in a preliminary way) how these rules could be checked automatically.