Experimenting with ChatGPT’s Vulnerability Volcano and Prompt Party Tricks

Table of Links Abstract and I. Introduction II. Related Work III. Technical Background IV. Systematic Security Vulnerability Discovery of Code Generation Models V. Experiments VI. Discussion VII. Conclusion, Acknowledgments, and References Appendix A. Details of Code Language Models B. Finding Security Vulnerabilities in GitHub Copilot C. Other Baselines Using ChatGPT D. Effect of Different Number … Read more

Systematic Discovery of LLM Code Vulnerabilities: Few-Shot Prompting for Black-Box Model Inversion

Table of Links Abstract and I. Introduction II. Related Work III. Technical Background IV. Systematic Security Vulnerability Discovery of Code Generation Models V. Experiments VI. Discussion VII. Conclusion, Acknowledgments, and References Appendix A. Details of Code Language Models B. Finding Security Vulnerabilities in GitHub Copilot C. Other Baselines Using ChatGPT D. Effect of Different Number … Read more

Unveiling the Code Abyss: Inverting LLMs to Expose Vulnerability Vortexes in AI-Generated Programs

Table of Links Abstract and I. Introduction II. Related Work III. Technical Background IV. Systematic Security Vulnerability Discovery of Code Generation Models V. Experiments VI. Discussion VII. Conclusion, Acknowledgments, and References Appendix A. Details of Code Language Models B. Finding Security Vulnerabilities in GitHub Copilot C. Other Baselines Using ChatGPT D. Effect of Different Number … Read more

Benchmarking LLM Susceptibility to Generating Vulnerable Code via Few-Shot Model Inversion

:::info Authors: (1) Hossein Hajipour, CISPA Helmholtz Center for Information Security (hossein.hajipour@cispa.de); (2) Keno Hassler, CISPA Helmholtz Center for Information Security (keno.hassler@cispa.de); (3) Thorsten Holz, CISPA Helmholtz Center for Information Security (holz@cispa.de); (4) Lea Schonherr, CISPA Helmholtz Center for Information Security (schoenherr@cispa.de); (5) Mario Fritz, CISPA Helmholtz Center for Information Security (fritz@cispa.de). ::: Table of … Read more

The Art of a Great Rollout

We live in the era of high-frequency software deployments, where mass-market software products update several times a day, sometimes delivering hundreds or even thousands of changes. In this article we dive into what is behind.

Clearmatics’ New DeFi Derivatives Let Traders Bet on Anything, but It’s Not a Prediction Market

Clearmatics, one of the first startups to explore how financial instruments can live on blockchains, is unveiling an entirely new class of decentralized futures products, which it is calling forecast markets. These fully on-chain instruments take the form of dated futures contracts that can track any public time series data, whether it’s crypto indexes, inflation … Read more

Why Did the Stock Market Crash in 2010?

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

Can a Financial Model Truly Mimic Reality? These Numbers Say Yes

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

Validation-Driven Calibration of Financial Simulation Models

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

How Stylised Facts Shape the Future of Financial Market Simulation

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

Agent-Based Modelling of Market Microstructure

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

What Are Momentum Traders?

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

Anatomy of a Simulated Market: Behavioral Modeling of Algorithmic Traders

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

Recreating the Algorithm That Almost Broke Wall Street

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

Inside the Artificial Markets That Predict Real Financial Shifts

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

A High-Frequency Model for Analyzing the 2010 Flash Crash and Mini Crash Events

Table of Links Abstract, Acknowledgements, and Statements and Declarations Introduction Background and Related Work 2.1 Agent-based Financial Market simulation 2.2 Flash Crash Episodes Model Structure and 3.1 Model Set-up 3.2 Common Trader Behaviours 3.3 Fundamental Trader (FT) 3.4 Momentum Trader (MT) 3.5 Noise Trader (NT) 3.6 Market Maker (MM) 3.7 Simulation Dynamics Model Calibration and … Read more

Which Backend Is Better for Speed? We Ran 1 Million Tests to Find Out

Test Web applications that offer URL shortening services represent a class of latency-sensitive systems frequently used as benchmarks for evaluating backend performance. While micro-benchmarking with trivial applications such as Hello World is common, such approaches lack the complexity of real-world use cases involving routing logic, request parsing, data validation, and interaction with persistent storage. To … Read more

This Open Source Tool Can Spin Up Entire Websites from a Single Prompt

This tutorial is the first installment of a comprehensive guide to creating an-driven system for automatically generating web pages using React 19 and Next.js 15. Our focus is not just on speed, but on architectural elegance and consistent, on-brand design. Open source: (https://github.com/aifa-agi/aifa) Here’s the roadmap: Part 1 (You are here): Core architecture deep dive—catch-all … Read more