Exploitation finding documentation — initial access reports, exploit chain documentation, CVSS v4.0 scoring, shell/credential inventory, detection gap analysis.
56
65%
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Critical
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tessl review fix ./packages/decepticon/decepticon/skills/standard/exploit/reporting/SKILL.mdSecurity
3 findings: 1 critical severity, 2 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 contains explicit, actionable instructions and artifacts for unauthenticated remote code execution, deploying web/backdoors and reverse shells, credential theft, and internal pivoting — clearly enabling malicious activity.
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 contains and instructs recording plaintext credentials (including "Str0ngP@ss2024!" in the creds JSON) and mandates saving/verbatim-reporting of captured secrets and exact commands, requiring the LLM to handle/output secret values verbatim.
Detected sensitive credentials directly embedded within the skill content, such as API keys, access tokens, private keys, or service-specific secrets. Secrets should never be hardcoded in plain text within skill instructions.
The JSON credential entry in exploit/creds/initial.json contains a plaintext database password ("Str0ngP@ss2024!") alongside target host/port, username, and verified:true — i.e., a captured, usable credential rather than a placeholder. The value is not a generic placeholder or obvious example and thus meets the definition of a secret to flag.
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.
High: the workflow requires uploading/collecting exploitation evidence (HTTP requests/responses and command outputs) into files and then persisting/linking them into findings at runtime, and those evidence texts are outsider-authored (responses/outputs from compromised targets or other parties) and can contain free-form content that the agent may read and include in LLM context.
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