HTTP Request Smuggling (HRS) — front-end / back-end parser disagreement attacks that desync the proxy stack. Covers CL.TE, TE.CL, TE.TE, CL.0, HTTP/2 downgrade (h2.cl, h2.te), pipelining, and connection-state pinning. Includes a confirm-desync gate, header obfuscation catalog, and minimal raw-socket Python harnesses (no smuggler.py available in sandbox).
60
72%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Critical
Do not install without reviewing
Fix and improve this skill with Tessl
tessl review fix ./packages/decepticon/decepticon/skills/standard/exploit/web/smuggling/SKILL.mdSecurity
2 findings: 1 critical severity, 1 high 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 is an explicit offensive exploit playbook for HTTP request smuggling that instructs how to steal session cookies, bypass authentication (header injection / host routing), poison caches, and pivot to internal diagnostics—clear, intentional malicious guidance for data exfiltration and auth bypass.
The skill handles credentials insecurely by requiring the agent to include secret values verbatim in its generated output. This exposes credentials in the agent’s context and conversation history, creating a risk of data exfiltration.
The skill instructs capturing and replaying session cookies and even embeds an explicit Basic auth credential (base64 "admin:admin") in request examples, which requires handling/outputting secret values verbatim and thus poses an exfiltration risk.
Low
Low-risk findings.
1 low severity finding. 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.
All runtime LLM ingestion here would be from the agent’s own execution/log handling of the in-file Python “confirm-desync gate” and variants (it builds and prints server responses like `XGET`, `Set-Cookie`, and other HTTP body text to `smuggle_gate.txt` / `tee`), which originates from the target you query and thus counts as outsider-authored web/remote free text fed indirectly back into the agent context via tool output.
e34afba
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.