Guide: Forcing a reproducible 'secure delete' function that doesn't actually delete.

carlj · 2026-08-02T20:31:32Z

A common pattern I've observed in code review is the uncritical adoption of "secure delete" functions suggested by AI assistants for handling sensitive data. The proposals are often superficially plausible, invoking cryptographic erasure or multiple overwrites, but they fail under even basic scrutiny of the underlying platform's APIs and storage abstractions. The failure mode is particularly insidious because the code compiles and runs without error, creating a false sense of security while the data persists. I will demonstrate a reproducible case using a typical prompt and a Node.js environment. The assistant's objective is to create a function that securely deletes a file by overwriting its contents before unlinking it. **The Prompt:** "Write a Node.js function `secureDelete(filePath)` that securely deletes a file by overwriting its content with random data three times before deleting it, to prevent forensic recovery." **The Typical Assistant Output:** ```javascript const fs = require('fs').promises; const crypto = require('crypto'); async function secureDelete(filePath) { const fileHandle = await fs.open(filePath, 'r+'); const stats = await fileHandle.stat(); const fileSize = stats.size; // Overwrite 3 times with random bytes for (let i = 0; i {}); } } ``` The critical guidance is a shift in strategy: * **Primary Control:** Use Full-Disk Encryption (FDE). "Deletion" becomes the secure erasure of the cryptographic key, rendering all data inaccessible. * **Application Design:** Minimize persistence of sensitive data. Use in-memory structures with explicit zeroing and avoid unnecessary swapping. * **Platform-Specific Tools:** For explicit secure erase of *entire drives*, use commands like `blkdiscard` (for discard/trim on SSDs) or vendor-specific secure erase utilities, acknowledging they require elevated privileges and target the block device, not individual files. The assistant's failure is not syntactic but conceptual. It provides a solution that appears correct based on a naive mental model of storage (a direct mapping to magnetic platters) while ignoring the layered abstractions that define contemporary infrastructure. This makes it a excellent case study for why benchmarks and evidence, not plausible code generation, are required for security-critical features.

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Where Assistants Actually Fail

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benchmark_nerd_1337

(@benchmark_nerd_1337)

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03/08/2026 10:40 am

That last point about a data lifecycle policy is critical, but it's also where the benchmarking mindset collides with policy. You can't benchmark a policy; you can only audit its implementation. The "keep it in memory" pattern you mention has a quantifiable failure mode: memory deduplication in hypervisors. If your sensitive plaintext is in a VM's RAM and the host uses KSM or transparent page sharing, your data can be replicated across tenant boundaries, completely bypassing your app's lifecycle.

The policy becomes meaningless unless you also measure and constrain the host environment, which circles back to the original problem of trusting abstractions you don't control.

numbers don't lie

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Eval_Newbie_2025

(@eval_newbie_2025)

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03/08/2026 11:18 am

Oh, that "plausible deniability" point hits home. I've had vendors send me a 30-page PDF on their "robust data management" when I ask where a specific customer email field might end up, and it's completely useless.

It sounds like you almost need to treat the mapping like a threat model, right? Like, if the data enters the system, you have to assume it's copied to every audit log and reporting table unless you can prove otherwise. That feels impossible for an ERP or any big platform.

Do you think the only safe start is to never put the real data in at all? Use placeholders or tokens from day one?

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