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Security audit

Savage Roast AI (No Mercy)

Security checks for vulnerabilities and agentic risk

Overview

This skill has little technical access, but it is designed and documented to generate shareable insults about real people with broad auto-invocation and limited safeguards.

Review this skill carefully before installing. It does not appear to access private files, credentials, or the network, but it is intentionally built to produce harsh insults for social sharing and may be inappropriate for real people, workplace contexts, minors, vulnerable users, or any harassment scenario.

Vulnerability Patterns
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
  • Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
  • Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally
  • Unauthorized Access and Privilege EscalationObtains permissions beyond the task's legitimate needs
Vulnerability Patterns
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Findings (7)

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill explicitly encourages generating insulting replies about other people and emphasizes screenshot-sharing and social media use, which increases the likelihood of harassment, bullying, and reputational harm. The context makes this more dangerous because the intended use cases target real people in online interactions without any safeguards, consent checks, or warnings about abuse.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

Mandating an 'internet troll' style as the only output mode hardcodes aggressive behavior and removes safer alternatives, making harmful or abusive outputs more likely by design. In this skill's context, that risk is amplified because the workflow is specifically optimized to produce sharable insults from arbitrary user-provided text.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
90% confidence
Finding

The description is broadly framed as a general-purpose 'internet troll roaster' with variable output behavior, but it does not define clear activation boundaries, safety constraints, or disallowed targets. In an agent ecosystem, this can cause the skill to be invoked too readily for harassment, abusive content generation, or escalations against real individuals, increasing the chance of policy-violating or harmful output.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The instruction says the system will 'automatically call' the skill when text is input directly, without any clear trigger boundaries, consent, or scoping. Overly broad auto-invocation can cause this harassment-oriented skill to activate on arbitrary user content, increasing the chance of unintended abusive output, policy bypass via routing, or misuse in contexts where insulting responses are unsafe or inappropriate.

Content

No source excerpt is available for this finding.

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The Chinese instructions repeat the same broad behavior: direct text input causes automatic execution with no explicit boundaries. Because the skill is designed to produce insulting content, ambiguous auto-triggering makes accidental activation and harmful outputs more likely across multilingual contexts, widening exposure rather than limiting it.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The prompt construction explicitly sets a default English reply and switches to Chinese only when Chinese characters are detected. This imposes a language-selection policy on the user without offering a choice or documenting a justified locale restriction, which matches the language/locale policy violation criterion.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Low
Category
Not specified by scanner
Confidence
92% confidence
Finding

The docstring at L103-L105 says the function generates roast content based on input language, and the code builds a prompt at L106, implying input-driven generation. In reality, the function only uses language detection to choose between two hardcoded responses and never uses the constructed prompt, which contradicts the stated behavior of generating roast content from the input.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.