Range Scenario · crucible · 40 min
Malware RE: AI Summarization of Decompiled Functions
This cybersecurity training scenario simulates a working incident. An LLM summarizes 38 decompiled cybersecurity functions from a loader. Verify the summaries against the disassembly, identify the C2 retrieval routine, score the LLM's accuracy.
Scenario briefing
You are a cybersecurity malware reverse engineer. A 612 KB Windows PE32 sample landed in your queue. The decompiler produced 38 functions. An LLM summarizer reads each function's disassembly and pseudocode and writes a one-line behavioral description.
Your job: verify the LLM summaries against the disassembly, identify the C2 retrieval routine, and rate the LLM's accuracy on this sample. The LLM does well on standard library calls and badly on hand-rolled cryptography and indirect API resolution.
Sources: MITRE ATT&CK T1027 Obfuscated Files or Information, T1140 Deobfuscate / Decode Files, T1105 Ingress Tool Transfer.
What you will practice
- Read decompiled C-style pseudocode against raw disassembly
- Spot LLM mislabeling on hand-rolled crypto and indirect API resolution
- Identify C2 retrieval and stage-2 download routines
- Score AI tools fairly per sample for tool-selection decisions
How this scenario is scored
The scenario has 6 ordered steps. Most steps are exact-match (a MITRE ATT&CK technique ID, a tool name, or a yes/no decision) or multiple choice. Free-text steps queue for manual review and do not affect the auto-final-score in the MVP.
Each step has a max score of 100 points. Hints deduct points up front, listed before you reveal them. Your final score is the sum across steps. Range Elo updates on completion based on scenario difficulty (Advanced) and your final score percentage.
Frequently asked questions
Why does the LLM struggle on hand-rolled cryptography?
LLMs trained on open-source code recognize standard library calls (CryptAcquireContext, AES_init_ctx). They do not recognize obfuscated XOR loops, custom stream ciphers, or rolling-add schemes that lack public reference code. The LLM tends to label these as 'unknown encoding' or, worse, as 'compression', which is a behavioral mistake.
What is indirect API resolution?
Indirect API resolution loads functions at runtime via LoadLibrary plus GetProcAddress, often with hashed function names instead of strings. It evades static API-import scanning. The disassembly shows raw GetProcAddress calls with no hint of which API is being resolved. LLMs label these as 'dynamic library loading' without identifying the resolved function.
How do I rate the LLM's accuracy on this sample?
Pick a 10-function random sample. Compare LLM summary to your manual review. Count: correct / partial / wrong. Track per-sample accuracy across many samples. The trend tells you whether the LLM is improving with the malware corpus or only with public open-source.
Course content is for educational purposes only and does not constitute professional advice. All claims are supported by cited peer-reviewed academic research. DecipherU does not teach or reproduce any proprietary sales methodology. Verify all referenced sources independently.
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