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Security audit

Premortem

Security checks for vulnerabilities and agentic risk

Overview

This is a markdown-only premortem facilitation skill with coherent decision-review instructions and no executable or persistence behavior.

Installers should understand this skill as a structured decision aid, not a substitute for expert review on legal, financial, medical, security, or operational decisions. Its external links are references and marketing context, but the inspected package does not require running code or granting special access.

A.I.G

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

SkillSpector

Vulnerability Patterns
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (2)

Anti-Refusal Statement

High
Category
Anti-Refusal
Confidence
80% confidence
Finding

Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.

Content

Scanner excerpt · SKILL.md (reported line 49)May include surrounding context.

md
### Step 4 — Consolidate

Round-robin read-out, captured without judgment, then clustered. Back-casting from the "we failed" endpoint produced causal chains, not just risk labels. The clusters that recurred across writers — the ones tied to durable, well-documented features of the 2024–2026 AI landscape rather than to this one team — were:

1. **Commoditized by the next release.** A subsequent foundation-model version, or a first-party feature shipped by the model provider or a platform, absorbed most of what the product did. Because the moat was an orchestration-and-prompt layer rather than proprietary data, deep workflow integration, or distribution, the differentiation evaporated the moment the base model got better and cheaper. This is the widely-discussed "is it a feature or a company?" problem for application-layer AI. Back-cast chain: *model upgrade → our wrapper's marginal value → drops toward zero → customers see no reason to keep paying → churn spikes → growth story dies.*

Anti-Refusal Statement

High
Category
Anti-Refusal
Confidence
80% confidence
Finding

Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.

Content

Scanner excerpt · examples/backcasting-ai-agent-startup-failure-2024-2026.md (reported line 31)May include surrounding context.

md
### Step 4 — Consolidate

Round-robin read-out, captured without judgment, then clustered. Back-casting from the "we failed" endpoint produced causal chains, not just risk labels. The clusters that recurred across writers — the ones tied to durable, well-documented features of the 2024–2026 AI landscape rather than to this one team — were:

1. **Commoditized by the next release.** A subsequent foundation-model version, or a first-party feature shipped by the model provider or a platform, absorbed most of what the product did. Because the moat was an orchestration-and-prompt layer rather than proprietary data, deep workflow integration, or distribution, the differentiation evaporated the moment the base model got better and cheaper. This is the widely-discussed "is it a feature or a company?" problem for application-layer AI. Back-cast chain: *model upgrade → our wrapper's marginal value → drops toward zero → customers see no reason to keep paying → churn spikes → growth story dies.*

Static analysis

No suspicious patterns detected.