Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add UnboundCompute/security-agent-skills --skill hunting-ios-keychain-and-data-protection-gapsgit clone --depth 1 https://github.com/UnboundCompute/security-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/unboundcompute/security-agent-skills/hunting-ios-keychain-and-data-protection-gaps)<a href="https://agentmods.dev/skills/unboundcompute/security-agent-skills/hunting-ios-keychain-and-data-protection-gaps"><img src="https://agentmods.dev/badge/skills/unboundcompute/security-agent-skills/hunting-ios-keychain-and-data-protection-gaps/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/unboundcompute/security-agent-skills/hunting-ios-keychain-and-data-protection-gaps"><img src="https://agentmods.dev/badge/skills/unboundcompute/security-agent-skills/hunting-ios-keychain-and-data-protection-gaps.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00190 | $0.02178 |
| Opus 5.5 | $0.00076 | $0.00871 |
| Sonnet 5.5 | $0.00038 | $0.00436 |
| Haiku 4.5 | $0.00019 | $0.00218 |
Grade A, and why
hunting-ios-keychain-and-data-protection-gaps scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hunting iOS keychain and data protection gaps: when a secret is readable while the device is locked
iOS gives every stored secret a protection class that decides when and where it can be read: a keychain item has an accessibility attribute, and a file has a data-protection class, and together they say whether the value is available only after the user unlocks the device, whether it is bound to this device alone, and whether it leaves in a backup. Those classes are safe only when each secret carries the strictest one it can tolerate. The gap is a secret stored under a class weaker than it needs: a token marked always-accessible that can be read while the device is locked, a credential not restricted to this device that migrates in a backup to another one, or a file whose protection class lets it be read outside an unlocked session. The bug is not that the secret is stored; it is a protection class that exposes it in a context, locked, backed up, migrated, or post-theft, where it should be unreadable. You hunt these by listing every stored secret and checking the protection class against the context it must survive.
When to use
- An iOS app stores credentials, tokens, or sensitive values in the keychain or in files.
- A stored item may carry an accessibility or data-protection class weaker than its sensitivity requires.
- Secrets may survive into a device backup or migrate to another device when they should not.
Scope check
Hunt at-rest protection gaps only on apps and devices you own or are authorized to assess, on test devices and accounts, proving a secret is recoverable in the weaker context without using it against a real account. A recovered secret authenticates as its owner, so keep every probe within scope and treat recovered material as sensitive. If you can't name the authorization, stop.
The loop
- Establish the protection class each secret actually carries and the context it must survive first. For each stored secret, determine its keychain accessibility or file data-protection class and the strongest context it needs to resist: locked-device read, backup inclusion, migration to another device, or post-theft access. This is the false-positive killer: a secret stored with the strictest class it can tolerate, available only when unlocked and bound to this device, is not exposed even though it exists on disk. Name the class and the context before claiming a gap.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 138 lines · 190 tokens per session scan A 99948695bbbe
hunting-ios-keychain-and-data-protection-gaps is a skill published in the GitHub repository UnboundCompute/security-agent-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 190 tokens to every session and 2,178 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-09.
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