Topic starter
11/08/2026 3:17 pm
TL;DR: A frontier model reproduced a senior researcher’s manual SonicWall firmware decryption workflow by tracing SWI encryption, identifying an embedded Vault-based key path, reconstructing Shamir shares, and recovering the RSA key, according to Bishop Fox. The result shows that AI agents can materially compress reverse-engineering effort when they can reason over binaries, vaults, and cryptographic workflows, even if human judgment still matters.
Editorial analysis by NHI Mgmt Group, based on content published by Bishop Fox: “Cracking Firmware with Claude: Senior-Level Skill, Junior-Level Autonomy”.
Key questions
Q: What breaks when firmware decryption relies on hidden key handling inside the appliance?
A: When key handling is buried inside the appliance, reverse engineering becomes dependent on finding a working runtime environment or a decrypted image first.
Q: Why do embedded Vault stores and Shamir shares create a firmware governance risk?
A: They collapse separation between the thing being protected and the mechanism that protects it.
A: Look for unmonitored access to decompilers, sandbox images, firmware copies, recovery material, and MCP-connected tooling.
Practitioner guidance
- Audit appliance images for embedded trust roots Review firmware and virtual appliance packages for shipped secret stores, recovery shares, and internal management services that can be reconstructed from the image itself.
- Restrict agent access to decompiler workflows Limit which AI agents can inspect binaries, file systems, and secret-management artefacts, especially in environments that contain vendor or customer credentials.
- Test whether encrypted firmware depends on recoverable internals Validate whether the decrypt path requires an external secret boundary or whether the appliance ships enough information to regenerate the key path locally.
Bottom line: The article shows that AI agents can reproduce a senior researcher’s firmware decryption workflow when given enough artefacts and minimal direction.
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11/08/2026 3:30 pm
Agentic reverse engineering has moved firmware secrecy from a human-only problem to a supervised machine workflow. The important shift is not that the model found a bug, but that it reconstructed a protected software path from binary evidence, filesystem artefacts, and cryptographic structure. That means vendor assumptions about the cost of unpacking firmware are weakening faster than many teams expect. Practitioners should assume AI-assisted analysis can now shorten the time from artifact access to decrypted contents.
A few things that frame the scale:
- Only 44% of organisations are currently using a dedicated secrets management system, according to the 2024 State of Secrets Management Survey.
- 73% of vaults are misconfigured, leading to unauthorised access and exposure of sensitive data, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: What should teams do when an agent gets stuck on a cryptographic detail?
A: Do not assume the model has failed in a terminal way. Insert a human checkpoint to test alternate hypotheses, such as a different padding mode, hash choice, or key derivation path, and then resume the workflow with the new constraint. That keeps the agent productive without letting it burn time inside a false branch.
👉 Read our full editorial: AI agent reverse engineering exposes new firmware decryption risk