What Reverse Engineering Teaches Us About Claude's Real Limits

There's a reliable way to understand how far a tool actually reaches: put it to work on something genuinely difficult and observe where it breaks. That's essentially what a post published in mid-April by Huli on their technical blog proposes: "Reunderstanding the Power of AI Through Reverse Engineering". The piece isn't a complaint or a panegyric, it's a hands-on analysis of what happens when you put Claude into reverse engineering workflows, one of the most demanding technical disciplines in terms of structural reasoning and tolerance for ambiguity.

The article circulated on Hacker News in early May and, although it arrived with few points and no comments at the time of posting, the text itself deserves attention because it covers an angle that standard benchmarks don't handle well: performance in contexts where the model has to infer intent from compiled artifacts, without documentation, without readable variable names, and with frequently obfuscated logic.

What the article explores

Huli regularly works in offensive security and CTFs (Capture The Flag), environments where reverse engineering is a core skill. Their experiment involved integrating Claude at different stages of that process: from interpreting disassembly to reconstructing business logic from bytecode. The approach is empirical, which is what makes it more useful than most posts claiming "Claude is amazing at X."

The conclusions drawn are twofold. On one hand, Claude proves useful at code comprehension tasks when context is sufficient: renaming functions, proposing hypotheses about a block's purpose, detecting known vulnerability patterns. On the other, it shows notable fragility when ambiguity is structural, meaning when there aren't enough clues in the artifact itself for the model to anchor its reasoning. In those cases, the model tends to "fill in the blanks" with what statistically makes the most sense, which in reverse engineering can be more dangerous than saying nothing at all.

Why this kind of evaluation matters

Reverse engineering is a demanding testing ground precisely because it doesn't tolerate plausible answers: either the reconstructed logic is correct or it isn't, and verifying that requires time and deep technical knowledge. That makes it an ideal environment to calibrate how far models actually reason versus simply generating coherent-sounding text that resembles reasoning.

This type of analysis is also relevant for those working with Claude Code in development or audit workflows. If Claude can be useful as a first step in understanding unfamiliar code, even compiled code, it opens real possibilities for security teams or developers maintaining legacy systems without documentation. But if that utility depends critically on context being sufficiently rich, teams need to know that before trusting outputs without human review.

Claude Opus 4.7's 1M token context window makes loading large artifacts technically feasible. Huli's article doesn't venture into model comparisons, but the point about reasoning quality under structural ambiguity applies regardless of available context size: more tokens doesn't solve the problem of reasoning about genuinely incomplete information.

Who should read this

• Security teams evaluating whether to integrate Claude into malware analysis or binary audit pipelines.
• Developers working with legacy code or third-party systems without documentation who want a realistic estimate of what they can delegate.
• Anyone using Claude Code with sub-agents for automated analysis tasks and wanting to better understand where to place human validation.

The article offers no categorical verdict, Claude is useful or it isn't, but something more honest: it depends on the type of task, the available context, and whether whoever reviews the outputs has the judgment to detect when the model is filling in blanks rather than reasoning.

At ClaudeWave, we value posts like this precisely because they're rare: concrete technical analysis, without agenda, written by someone who has skin in the game. If you work in security and have tested Claude in similar workflows, the comments section of the HN thread could be a good place to add context, though for now it remains empty.