LLM Autonomously Solves DEF CON CTF Finals Challenge for First Time

Summary

A researcher from the Shellphish CTF team has documented what appears to be the first instance of a Large Language Model autonomously solving a DEF CON CTF Finals challenge with minimal human intervention. The challenge, called "ico," was a complex reverse engineering and exploitation problem that world-class hackers had been working on for hours. Using GPT-5 (the newly released model) integrated with an IDA Pro MCP server through Cursor IDE, the LLM ran unassisted for 12 minutes, making extensive use of tool calls to analyze a 1.4MB x86-64 binary with nearly 6,000 functions.

The researcher, who leads Shellphish's AIxCC (AI Cyber Challenge) team, emphasized the significance of this achievement, stating it represents the first time an LLM has solved a challenge at DEF CON Finals difficulty level purely through autonomous reasoning. DEF CON CTF is widely considered the "Olympics of hacking," featuring the world's most difficult cybersecurity challenges and attracting top security researchers. The challenge involved analyzing a server binary with a virtual machine-like dispatch loop, with no position-independent executable (PIE) or stack canary protections.

The breakthrough was enabled by the Model Context Protocol (MCP), which allowed GPT-5 to directly interact with IDA Pro for binary analysis. The researcher noted this was their first experience with GPT-5 and was impressed by the model's unprecedented use of tool calls compared to previous models like Claude 4 Sonnet, Claude 4 Opus, and O3. While the article's transcript appears to cut off before showing the complete solution, the researcher plans to share the full LLM interaction transcript, marking an important milestone in AI-assisted cybersecurity research.

• GPT-5 demonstrated significantly more extensive tool use compared to previous frontier models like Claude 4 and O3, running unassisted for 12+ minutes
• This achievement suggests AI agents are reaching capability thresholds for advanced cybersecurity tasks previously thought to require expert human intuition

Editorial Opinion

This represents a watershed moment for AI in cybersecurity: autonomous solving of elite-level CTF challenges suggests we're approaching a phase change in AI capabilities for offensive security. The significance isn't just that an LLM can assist with reverse engineering—it's that it can navigate the complex reasoning chain from binary analysis to exploitation strategy without human guidance. However, the full implications remain unclear since the transcript cuts off before showing whether the LLM achieved full exploitation or just produced initial reconnaissance scripts. The cybersecurity community should closely monitor whether this generalizes to other DEF CON-level challenges or represents an outlier success.