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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.
NHIMG editorial — based on content published by Bishop Fox: AI agent reverse engineering of SonicWall firmware encryption
Questions worth separating out
A: The main failure is that an agent can chain together artefacts that humans intended to review separately.
Q: Why do embedded secrets make firmware encryption much weaker than it appears?
A: Because the image is only protected if the key path stays outside the same trust boundary.
A: Look for unmonitored access to decompilers, sandbox images, firmware copies, recovery material, and MCP-connected tooling.
Practitioner guidance
- Constrain AI agent access to reverse-engineering tools Allow decompilers, sandboxes, and MCP-connected analysis tools only inside approved environments with explicit session logging and task scoping.
- Separate firmware artefacts from key-bearing material Keep recovery shares, unseal material, and operational binaries in different trust zones so that possession of a decrypted filesystem does not expose the full decryption path.
- Review appliance images for embedded secret dependencies Map every secret-bearing dependency inside firmware and appliance filesystems, including Vault data, service tokens, and unseal workflows, then remove assumptions that shipping code can safely contain recovery paths.
What's in the full article
Bishop Fox's full analysis covers the operational detail this post intentionally leaves for the source:
- Step-by-step decompilation of the SonicWall management binary and the exact path used to locate the Vault-backed key logic.
- The full cryptographic reconstruction sequence, including the Shamir share handling and OAEP hash debugging that led to decryption.
- The redacted code excerpts and command flow used to run the original Vault binary and export the RSA key.
- Bishop Fox's own reasoning notes on where the model stalled and how human supervision changed the result.
👉 Read Bishop Fox's analysis of AI-assisted SonicWall firmware reverse engineering →
AI agent reverse engineering and firmware decryption risk?
Explore further
11/08/2026 3:30 pm
AI-assisted reverse engineering is now a governance problem, not just a research capability. The article shows a model following decryption logic, reconstructing secret material, and completing a firmware analysis chain that previously required a senior human researcher. That means access to decompilers, decrypted filesystem images, and recovery artifacts now needs policy, logging, and approval boundaries. The field should treat these AI workflows as privileged analysis systems, not casual productivity tools.
A few things that frame the scale:
- 91% of former employee tokens remain active after offboarding, leaving organisations vulnerable to potential security breaches, according to The 2025 State of NHIs and Secrets in Cybersecurity.
- 62% of all secrets are duplicated and stored in multiple locations, causing unnecessary redundancy and increasing the risk of accidental exposure, according to The 2025 State of NHIs and Secrets in Cybersecurity.
A question worth separating out:
Q: Who should approve the use of AI agents in offensive research workflows?
A: The accountable owner should be the team that controls the sensitive artefacts, the tooling, and the research objective, usually under security or product security governance. AI agents should not operate as implicit analysts. Their use needs a named approver, scoped permissions, and retention rules for any decrypted or reconstructed material.
👉 Read our full editorial: AI agent reverse engineering exposes new firmware decryption risk
