Full Report
Can AI really write safer code? Martin dusts off his software engineer skills to put it it to the test. Find out what AI code failed at, and what it was surprisingly good at. Also, we discuss new research on how AI LLM models can be used to assist in the reverse engineering of malware.
Analysis Summary
# Main Topic
Evaluation of Large Language Models (LLMs) for Software Development and Malware Reverse Engineering Assistance. The analysis focuses on the dual application of AI: improving code safety/quality by software engineers, and accelerating the analysis of malicious binaries.
## Key Points
- **AI in Code Generation (Software Engineering Context):**
- AI demonstrated utility in high-level architecture discussion and generating large volumes of initial code structure ("boilerplate").
- AI successfully resolved a complex threading issue that manually challenged the engineer.
- **Failures:** Generated code failed to interface correctly with required libraries (incorrect parameters, calling fictional functions).
- **Security/Robustness:** AI-generated code lacked sufficient sanity checks or variable verification, especially for external inputs, prioritizing functionality over long-term security.
- **Limitation:** Demonstrated weakness in handling esoteric or less common APIs.
- **AI in Malware Analysis (Threat Intelligence Context):**
- LLMs can be used as a "reverse engineering sidekick" to facilitate and speed up the typically time-consuming analysis of obfuscated malicious software.
- Investigation into these tools and approaches is recommended to improve existing reverse engineering workflows.
## Threat Actors
- Not explicitly named in the context of the AI code testing or LLM-as-a-sidekick review.
- *Mentioned indirectly:* Malware writers who obfuscate code to slow down reverse engineering efforts.
## TTPs
- **Software Development Failure Modes:** Generating code with interface errors, missing parameter validation, and insufficient input verification (potential for injection/logic flaws if not manually corrected).
- **Malware TTPs:** Obfuscation techniques used by malware authors to hinder analysis.
## Affected Systems
- **Code Testing:** Personal software project environment, focusing on the interaction with specific software libraries/APIs.
- **Malware Analysis:** Systems targeted by malware whose reverse engineering process could be accelerated by LLMs.
## Mitigations
- **AI-Assisted Coding:** Requires rigorous manual testing, verification of library interactions, and explicit instruction/rewriting for robust security checks, as AI defaults may prioritize feature completion over defense.
- **Malware Analysis:** Investigate and implement LLM-assisted tools to speed up reverse engineering workflows against obfuscated malware.
## Conclusion
AI (LLMs) presents a paradoxical landscape for security. While highly effective for boosting productivity on routine coding tasks and accelerating defensive analysis (reverse engineering), it currently produces functionally incomplete or insecure code when dealing with specific external dependencies or esoteric logic, highlighting that human oversight remains critical for generating truly secure software.