A low-cost hacking sidekick: Baby steps to using offensive AI agents
Blog post from Bugcrowd
The author of the text is a hacker who uses large language models (LLMs) to augment their offensive security work. They explore ways to harness AI agents for hacking purposes, focusing on building effective agents from scratch and using existing tools like nerve and robopages to simplify the process. The author emphasizes the importance of tasking agents properly and structuring workflows to maximize their effectiveness. They demonstrate the use of LLMs in reconnaissance, payload creation, and report writing, showcasing the potential of AI-powered hacking assistants. The text concludes by highlighting the benefits of learning about LLMs and machine learning concepts for hackers looking to stay ahead of evolving defenses and maximize their impact.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 16 | 5,694 | 663 | 215 | +42% |
| AI Agents | 13 | 2,565 | 399 | 151 | +29% |
| Multi-agent systems | 3 | 373 | 66 | 39 | +72% |
| AI Model Fine-tuning | 2 | 889 | 213 | 97 | +38% |
| AI Guardrails | 1 | 365 | 94 | 40 | +51% |
| Real-time | 1 | 5,174 | 1,177 | 267 | +34% |
| Secrets Management | 1 | 1,334 | 167 | 87 | +102% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.