Top 10 Cybersecurity Threats and Emerging Hacking Trends in 2025 Predicted by Experts

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Portswigger’s List of Web Hacking Techniques Celebrates Curiosity and Research

In 2025, Portswigger published a list of top web hacking techniques that showcased the ingenuity and creativity of the web hacking community. Several entries stood out, demonstrating novel approaches and clever exploitation of common vulnerabilities.

Using Large Language Models for Blackbox Research

One notable technique involved using large language models (LLMs) for blackbox research, enabling researchers to analyze complex software without needing to understand the underlying code. This approach showed promise in identifying novel vulnerabilities and generating patches.

According to James Kettle, director of research at Portswigger, “While LLMs are valuable tools, they are not yet capable of replacing human researchers entirely.”

The Importance of a Strong Toolchain for Agent-Based Research

Kettle emphasized the need for a strong toolchain for agent-based research, which can significantly improve the efficiency and effectiveness of vulnerability research. He noted that having a robust toolchain is crucial for identifying and exploiting vulnerabilities and can help bridge the gap between human researchers and automated agents.

Recent examples of successful agent-based research include the use of LLMs to identify vulnerabilities in the Python Package Index (PyPI)
The orchestration of AI code reviews at scale demonstrates the potential of combining human expertise with machine learning capabilities to enhance software security

The Future of Vulnerability Research

The increasing reliance on agents and LLMs raises questions about the future of vulnerability research. Experts point out that model choice is critical and not limited to the latest offerings from OpenAI and Anthropic. The key is to create an architecture that handles efficient code reviews and balances the benefits of LLMs with the limitations of current technology.

Ultimately, the intersection of human expertise and machine learning capabilities holds great promise for enhancing software security. By combining the strengths of both worlds, researchers can develop more sophisticated tools and techniques that will help protect against emerging threats.