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-mobile-tapjacking-and-overlay-abusegit 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-mobile-tapjacking-and-overlay-abuse)<a href="https://agentmods.dev/skills/unboundcompute/security-agent-skills/hunting-mobile-tapjacking-and-overlay-abuse"><img src="https://agentmods.dev/badge/skills/unboundcompute/security-agent-skills/hunting-mobile-tapjacking-and-overlay-abuse/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-mobile-tapjacking-and-overlay-abuse"><img src="https://agentmods.dev/badge/skills/unboundcompute/security-agent-skills/hunting-mobile-tapjacking-and-overlay-abuse.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 | $0.00185 | $0.02078 |
| Opus 5.5 | $0.00074 | $0.00831 |
| Sonnet 5.5 | $0.00037 | $0.00416 |
| Haiku 4.5 | $0.00018 | $0.00208 |
Grade A, and why
hunting-mobile-tapjacking-and-overlay-abuse 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hunting mobile tapjacking and overlay abuse: when a tap lands on a screen the user cannot see
A mobile user acts by touching what they see, and that trust breaks when another app can draw over the screen or intercept the interaction. An overlay app puts its own window on top of a sensitive screen, a permission grant, a payment confirmation, a credential prompt, and either hides what the user is really touching or fakes a benign interface over it, so the tap the user believes is dismissing a game lands on the real approve button underneath. Related abuses intercept rather than draw: an accessibility-style service drives the interaction programmatically. The sensitive screen is complicit when it accepts a touch it cannot confirm the user saw: it does not detect that it is obscured, does not filter touches that passed through an overlay, or confirms an action without binding the confirmation to what was actually displayed. The bug is a consequential approval taken while the screen cannot vouch for what the user saw or touched. You hunt these by finding every sensitive screen and checking whether it defends against being overlaid or driven.
When to use
- A mobile app presents consent, confirmation, or credential-entry screens another app could overlay.
- A sensitive action is approved by a tap that an overlay could hide, fake, or pass through.
- An accessibility-style service or intercepting app could drive the interaction programmatically.
Scope check
Hunt overlay and tapjacking abuse only on apps and devices you own or are authorized to assess, on test devices and accounts, using a benign test overlay you control against a test screen rather than deceiving a real user. A confirmed case makes a user approve an unintended sensitive action, so keep every probe within scope. If you can't name the authorization, stop.
The loop
- Establish whether the sensitive screen can vouch for what the user saw and touched first. For each consequential screen, determine whether it detects when it is obscured and refuses or filters the touch, and binds its confirmation to the action actually displayed, or whether it accepts a tap without knowing whether an overlay hid or faked the interface. This is the false-positive killer: a screen that rejects obscured touches and confirms only the displayed action cannot be redressed by an overlay. Name the screen's defense before crafting an overlay.
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 · 139 lines · 185 tokens per session scan A ed3cd658a903
hunting-mobile-tapjacking-and-overlay-abuse is a skill published in the GitHub repository UnboundCompute/security-agent-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 185 tokens to every session and 2,078 once invoked, about $0.0007 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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