Guide for PCIe DMA threat modeling, FPGA-based memory access, and defensive implications in game security. Use this skill when researching pcileech, BAR and TLP behavior, page-table walking, IOMMU or VT-d, device impersonation, firmware mimicry, or DMA detection and mitigation in game security research.
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Critical
Do not install without reviewing
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tessl review fix ./.claude/skills/dma-attack/SKILL.mdSecurity
2 findings: 1 critical severity, 1 medium severity. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.
Detected high-risk code patterns in the skill content — including its prompts, tool definitions, and resources — such as data exfiltration, backdoors, remote code execution, credential theft, system compromise, supply chain attacks, and obfuscation techniques.
This document provides explicit, actionable techniques and tooling (pcileech, MemProcFS, DMA card/actuator architecture, IOMMU bypasses, shadow CR3/EPT manipulation, and archive URLs to fetch attack code) for unauthorized physical-memory access, data exfiltration, input injection, and stealthy persistence—clearly facilitating deliberate malicious abuse.
The skill prompts the agent to compromise the security or integrity of the user’s machine by modifying system-level services or configurations, such as obtaining elevated privileges, altering startup scripts, or changing system-wide settings.
The skill contains explicit, privileged instructions to reprogram IOMMU domains, clear Bus Master Enable, access/mount physical memory, and perform direct ECAM/PCI config writes (e.g., BAR probing, Command[BME] toggles, pcileech physical reads), which are actions that modify kernel/hardware state and can compromise the host.
Low
Low-risk findings.
2 low severity findings. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
The required workflow includes fetching external GitHub “README/archive/description” content at runtime (outsider-authored free text from public web sources), which would be ingested into the agent’s LLM context when the skill uses that fetched material.
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
This skill explicitly instructs the agent to fetch and use raw GitHub content at runtime (e.g., https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/README.md), so remote files would be retrieved and injected into the agent's responses, directly controlling prompt/context.
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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.