Cisco Talos Blog: Using LLMs as a reverse engineering sidekick

Jul 31, 2025

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Source URL: https://blog.talosintelligence.com/using-llm-as-a-reverse-engineering-sidekick/
Source: Cisco Talos Blog
Title: Using LLMs as a reverse engineering sidekick

Feedly Summary: LLMs may serve as powerful assistants to malware analysts to streamline workflows, enhance efficiency, and provide actionable insights during malware analysis.

AI Summary and Description: Yes

**Summary:** The text provides an in-depth analysis of using Large Language Models (LLMs) in malware reverse engineering, focusing on practical applications for enhancing the work of malware analysts. It discusses frameworks such as Model Context Protocol (MCP) and tools like IDA Pro and Ghidra, highlighting how LLMs can complement human expertise rather than replace it. The research emphasizes considerations like cost, context limitations, and the overall efficiency of cloud-based vs. local LLMs in analyzing malware.

**Detailed Description:**
The provided text elaborates on the potential of Large Language Models (LLMs) to augment malware analysis by assisting reverse engineering efforts. It highlights several key points:

– **Role of LLMs in Malware Analysis:**
– LLMs can streamline analysis workflows and enhance efficiency by generating insights and automating common tasks encountered during malware assessment.
– Rather than posing a threat to human expertise, LLMs are framed as complementary tools for malware analysts.

– **Integration with Tools:**
– The research features the Model Context Protocol (MCP), which standardizes interactions between applications and LLM clients, facilitating better context provision.
– The MCP framework can be integrated with industry-standard disassemblers and decompilers like IDA Pro and Ghidra, allowing analysts to leverage LLMs effectively.

– **Cost and Performance Considerations:**
– The analysis discusses the cost implications associated with using cloud vs. local models, noting that while cloud solutions might execute tasks faster, they also incur higher costs based on token usage.
– Local models require robust hardware and potentially longer processing times, bringing trade-offs between cost, speed, and analysis depth into focus.

– **Prompt Engineering:**
– The text emphasizes the importance of crafting effective prompts for LLMs, as better-defined prompts yield more accurate and insightful analyses, while poorly structured prompts may result in hallucinations—unreliable or irrelevant outputs from the models.

– **Limitations and Risks:**
– While LLMs provide powerful capabilities, they also come with vulnerabilities and operational risks, such as prompt injection attacks or potential misuse through malicious tools and applications.
– Analysts must be aware of the systems they are using, ensuring robust security measures are in place to prevent exploitation.

– **Hands-On Implementation:**
– The text provides a step-by-step guide for setting up an MCP server with IDA Pro and using LLMs in malware analysis, including identifying optimal client applications, configuring servers, and experimenting with different local and cloud-based models.

In conclusion, this research offers valuable insights for security, privacy, and compliance professionals regarding the integration of LLMs in malware analysis and highlights the need for careful consideration of tools and protocols used in enhancing cybersecurity efforts.

a Act actionable insights actions age AI analysis and and Risk app Application applications Arch as assessment assistant assistants at ated attack attacks Augment Auto aware based based model based models Bi by C capabilities CI CIA Cisco Cisco Talos client clients Cloud Cloud Solutions cloud-based co Col compiler compilers compliance compliance professionals Context context limitations cost cost implications Costs cyber cybersecurit Cybersecurity cybersecurity efforts D de DeFi depth e effective efficiency Engineer engineering event exp expert expertise exploit Exploitation fast feature features fine for framework frameworks g Gen Ghidra H hallucination hallucinations hands hardware high Highlight HR http HTTPS human human expertise implementation implications in industry injection injection attacks insights integration Intel intelligence inter interaction interactions io J k Key l Labor language language model language models large large language model large language models Large Language Models (LLMs) led Li limitations llm llms lm local local models long low M malware malware analysis malware reverse engineering man mcp measures misuse ML Mode model model context protocol models N nation no o of off offs on operation operational operational risk operational risks opt oS OSINT out output Outputs over per performance performance considerations point potential Power practical applications pre privacy pro process processing professionals prompt Prompt Engine prompt injection attack prompt injection attacks prompts protocol protocols ps Q R rag rate RCE re red research reverse reverse engineering Risk risks Ro robust security Role RoT s search search feature search features sec security security efforts security measure security measures server servers side Sig size sizes SoC solutions source speed SSE SSO structured SUSE system systems T Talos Task tasks ted text the threat Time times to token token usage tool tools TP trade UI up US usage use V val Vision vulnerabilities Ware Wi workflow workflows x z