Use chat to explore an idea.
Explaining an excerpt, thinking through a hypothesis, or drafting an analysis note can fit a conversation. Keep its claims separate from evidence obtained from the binary.
Devirt vs general-purpose AI for reverse engineering
An explanation is useful.
Evidence makes it inspectable.
Evidence makes it inspectable.
A conversation can help you understand code. A binary-analysis delivery also needs to establish what was recovered, how it was checked, and what remains unresolved.
Same binary.
Different division of work.
| The question | General-purpose chat | Devirt |
|---|---|---|
| What you provide | Excerpts, observations and context for the model to interpret. | An authorized binary and the output or research question you need. |
| Where evidence comes from | You obtain and supply binary evidence, then check the response against it. | The analysis service returns its selected output together with scoped verification records. |
| What comes back | Explanations, hypotheses, suggested code and follow-up questions. | An IDA database, annotated pseudocode, report, LLVM IR, or JSON—not just a conversation. |
| What “proven” means | A confident response is not, by itself, a recorded verification result. | Proven units are distinguished from runtime-dependent, unresolved, and permanently bounded units. Assumptions remain part of the result. |
| Data handling | Retention and training terms depend on the service, account and deployment. | Published policy: no training on customer files, no cross-customer sharing, separate input/output/audit retention windows. |
Devirt describes the agreed service deliverable, not a guarantee for every build. Eligibility, complexity, and proof scope are assessed separately.
See what a coverage statement actually contains.
Select a category in this synthetic ledger. Each unit stays accounted for, including the ones the analysis cannot prove. These are teaching numbers, not a production benchmark.
Scope · 100 selected operationsTeaching sample
Nothing disappears from the denominator.
This teaching ledger contains 100 selected operations: 86 proven, 7 runtime-dependent, 2 unresolved, and 5 permanently bounded. Every unit is counted exactly once.
Synthetic, not a service benchmark. Solver SAT and timeout counts are separate outcomes, not extra coverage buckets.
A useful choice.
Not a universal winner.
Use Devirt when you need an analysis artifact with its limits attached.
A coverage ledger makes the boundaries visible. It does not turn every answer into a proof, and a proof within a model is not a guarantee about every possible runtime.
Create an account for assessmentCan an AI system use a disassembler or verifier?
Yes, when those tools and the surrounding workflow are configured. That is different from a plain chat session. Evaluate its artifacts, checks, scope and failure handling—not only the model name.
Does an UNSAT result prove the whole binary?
No. It means no counterexample was found by the solver within the encoded model for that check. SAT identifies a counterexample; a timeout leaves the check unresolved. Read the assumptions and coverage ledger alongside the output.
Tool-enabled systems are a different comparison: Anthropic documents external tool use and code execution. Capabilities depend on the configured workflow.
What would you do instead?
Compare the work, the handoff, and the evidence—not just a number on a pricing page.
Choose the work you want back.
Start with the output you need. If the architecture or scope is unfamiliar, begin with a free assessment.
Create your workspace
Create an account, verify your email and upload your file. We review custom work and email your scope and quote.