Web crypto exploitation — padding-oracle (Vaudenay), AES-CBC bit-flipping / IV manipulation, AES-ECB pattern attacks (cut-and-paste, prefix/suffix recovery), HMAC bypass, hash-length extension, JWT alg confusion. Covers detection signals, working in-file Python harnesses (concurrent.futures, timeout=5, python3 -u, bounded request budget), and the confirm-oracle gate that must fire before iteration.
61
72%
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Critical
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tessl review fix ./packages/decepticon/decepticon/skills/standard/exploit/web/crypto/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, practical exploit guide that instructs how to discover keys, decrypt/forcibly recover ciphertexts, exfiltrate secrets, and forge authentication tokens (padding-oracle, CBC bit-flip, ECB cut-and-paste, HMAC/JWT bypass), enabling unauthorized data theft and privilege escalation.
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 explicitly instructs finding and reading hardcoded keys and tokens (grep/.env, cat), and contains code/templates that require inserting discovered keys or captured tokens verbatim (e.g. KEY = ..., TOKEN = "<base64...>") into outputs/requests, which forces the LLM to handle and output secrets directly.
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.
SKILL.md:55-96 and SKILL.md:127-196 instruct the runtime workflow to fetch attacker-supplied TARGET content indirectly via HTTP requests and capture response body prefixes (`r.text[:200]`) into variables used for the LLM’s subsequent reasoning; while the text comes from an outsider server, it is read as free-form response text that can include prompt-injection payloads.
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