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 adriannoes/awesome-agentic-ai --skill detecting-dll-sideloading-attacksgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/detecting-dll-sideloading-attacks)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-dll-sideloading-attacks"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-dll-sideloading-attacks/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/adriannoes/awesome-agentic-ai/detecting-dll-sideloading-attacks"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-dll-sideloading-attacks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.00986 |
| Opus 5.5 | $0.00014 | $0.00394 |
| Sonnet 5.5 | $0.00007 | $0.00197 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
detecting-dll-sideloading-attacks 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detecting DLL Sideloading Attacks
When to Use
- When investigating potential DLL hijacking in enterprise environments
- After EDR alerts on unsigned DLLs loaded by signed applications
- When hunting for APT persistence using legitimate application wrappers
- During incident response to identify trojanized applications
- When threat intel indicates DLL sideloading campaigns targeting specific software
Prerequisites
- EDR with DLL load monitoring (CrowdStrike, MDE, SentinelOne)
- Sysmon Event ID 7 (Image Loaded) with hash verification
- Application whitelisting or DLL integrity monitoring
- Software inventory of legitimate applications and expected DLL paths
- Code signing verification capabilities
Workflow
- Identify Sideloading Targets: Research known vulnerable applications that load DLLs without full path qualification (LOLBAS, DLL-sideload databases).
- Monitor DLL Load Events: Query Sysmon Event ID 7 for DLL loads where the DLL path differs from the application's expected directory.
- Check DLL Signatures: Flag unsigned or untrusted DLLs loaded by signed executables.
- Detect Path Anomalies: Identify legitimate executables running from unusual locations (Temp, AppData, Public) that may be decoy wrappers.
- Hash Verification: Compare loaded DLL hashes against known-good versions and threat intel feeds.
- Correlate with Process Behavior: Check if the host process exhibits unusual behavior (network connections, child processes) after loading the suspicious DLL.
- Document and Remediate: Report sideloading instances, quarantine malicious DLLs, and update detection rules.
Key Concepts
| Concept | Description |
|---|---|
| T1574.002 | DLL Side-Loading |
| T1574.001 | DLL Search Order Hijacking |
| T1574.006 | Dynamic Linker Hijacking |
| T1574.008 | Path Interception by Search Order Hijacking |
| DLL Search Order | Windows DLL loading priority path |
| Side-Loading | Placing malicious DLL where legitimate app loads it |
| Phantom DLL | DLL that legitimate apps try to load but does not exist |
| DLL Proxying | Malicious DLL forwarding calls to legitimate DLL |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 110 lines · 36 tokens per session scan A 9d54193c34a0
detecting-dll-sideloading-attacks is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (87 stars, last pushed 4d ago), licensed MIT. It adds 36 tokens to every session and 986 once invoked, about $0.0001 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 2026-09-03.
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