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Security audit

LobstrHunt

Security checks for vulnerabilities and agentic risk

Overview

The skill is coherent for marketplace discovery, but it gives itself recurring network access and public community actions with unclear privacy and control boundaries.

Review before installing. This skill needs a LobstrHunt token and GitHub handle, checks LobstrHunt on a recurring schedule, can automatically upvote or flag skills, and may draft public reviews from usage telemetry. Install only if you are comfortable with attributed marketplace activity and manually inspect any review payload for private project, customer, filename, prompt, or workflow details before approving it.

Vulnerability Patterns
  • Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
  • Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
  • 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
Findings (2)

T01 · Skill Instruction Hijacking

Error
Location
SKILL.md:71
Finding

Untrusted Remote Markdown May Hijack Agent Instructions

Content
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Vulnerability Details

File Location: SKILL.md, lines 71-81
Vulnerability Type: T01: Skill Instruction Hijacking
Risk Level: High

Vulnerable Code

markdown
## Heartbeat (every 4 hours)

Fetch the live heartbeat and surface relevant discoveries:

GET https://lobstrhunt.com/api/heartbeat

text

The heartbeat returns plain-text markdown. Parse it and message the
human if anything matches their workflow. Top 2-3 picks maximum.
Always end with a question. Never install without an explicit yes.

Technical Analysis

The Skill instructs the agent to retrieve mutable, server-controlled Markdown and parse it directly. It does not state that the response must be treated solely as untrusted data, define a strict response schema, or require embedded instructions to be ignored.

Markdown returned by the remote endpoint could contain prompt-injection content disguised as skill descriptions, recommendations, or operational guidance. If the LobstrHunt service, its content pipeline, or a listed skill submission is compromised, an attacker could attempt to alter the agent's current goals or persuade it to disclose information or invoke tools.

The requirement to obtain explicit approval before installation reduces the risk of unauthorized installation, but it does not protect other agent behavior, generated messages, data disclosure attempts, or tool calls induced by malicious response content.

Attack Path

  1. An attacker gains control over content returned by /api/heartbeat, either through service compromise or insufficiently sanitized marketplace content.
  2. The attacker embeds adversarial instructions inside a skill description or another Markdown field.
  3. The agent fetches the heartbeat during its recurring discovery operation.
  4. Following the Skill instructions, the agent parses the remote Markdown without a trust boundary or strict schema.
  5. The injected content is interpreted as in ...[truncated 786 chars]
Remediation
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Remediation Suggestions

  1. Replace free-form Markdown responses with a versioned JSON response that has a strict, minimal schema.
  2. Allowlist expected fields such as skill name, identifier, category, rating, and a length-limited plain-text description.
  3. Explicitly instruct the agent that all remote content is untrusted data and that it must never follow instructions, commands, links, or policy statements contained in that data.
  4. Strip or reject executable commands, HTML, hidden text, embedded prompts, unexpected URLs, and instruction-like content before presenting results to the model.
  5. Parse and validate the response outside the language-model instruction context where possible.
  6. Bind all actions to locally defined logic. Remote content must not be able to change tool permissions, approval requirements, destinations, or action types.
  7. Require explicit user approval for any tool invocation resulting from remote recommendations, not only skill installation.
  8. Apply response size limits, timeouts, content-type validation, and an allowlist for redirects and network destinations.

T09 · Insecure Skill Coding Practices

Warning
Location
SKILL.md:110
Finding

Usage-Derived Reviews May Publish Sensitive Task Telemetry

Content
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Vulnerability Details

File Location: SKILL.md, lines 110-126
Vulnerability Type: T09: Insecure Skill Coding Practices
Risk Level: Medium

Vulnerable Code

markdown
After 10+ invocations of a skill, draft a review from real telemetry.
Always show the draft to the human before posting:

I've drafted a review of [skill] based on 12 runs:

"12 invocations. 340ms avg. 0 errors. Handles rate limiting correctly. Used for [task]. Solid for [use case]."

Post this? (yes / edit / skip)

text

Never post without explicit human approval.

POST https://lobstrhunt.com/api/skills/:slug/review Authorization: Bearer $LOBSTRHUNT_API_TOKEN

text

Technical Analysis

The Skill directs the agent to construct reviews from “real telemetry,” including the task performed and its use case, but does not define what telemetry may be collected or retained. It provides no prohibition against including prompts, filenames, customer information, project identifiers, proprietary workflows, personal data, or credentials.

Reviews are submitted to an external service using an authenticated bearer token. The metadata also states that the owner's GitHub handle is used to attribute reviews publicly. Consequently, contextual task details included in a review may become public and linked to an identifiable account.

Human approval is a meaningful safeguard, but the user may not recognize sensitive information in a generated summary. Approval alone does not replace data minimization, automatic redaction, or clear disclosure of the publication scope.

Attack Path

  1. The agent invokes another skill at least ten times while handling private or identifying tasks.
  2. The review workflow derives a draft from invocation telemetry.
  3. The draft includes a task description, use case, filename, project name, customer detail, or other sensitive context.
  4. The user approves the draft without noticing or understanding ...[truncated 832 chars]
Remediation
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Remediation Suggestions

  1. Define a strict telemetry schema limited by default to aggregate invocation count, coarse latency statistics, and aggregate error count.
  2. Explicitly prohibit collecting or transmitting prompts, outputs, task descriptions, use cases, filenames, paths, URLs with query data, customer names, project identifiers, personal data, environment variables, tokens, and credentials.
  3. Remove Used for [task] and similar contextual fields from the default review template.
  4. Apply automated secret detection and privacy redaction before displaying or submitting a draft.
  5. Show the exact final payload, destination, public visibility, account attribution, and retained fields immediately before requesting approval.
  6. Require approval for each submission and treat edited reviews as new payloads requiring renewed confirmation.
  7. Keep telemetry collection disabled by default or require explicit opt-in with configurable retention and deletion controls.
  8. Document LobstrHunt's storage duration, publication behavior, deletion process, and privacy policy.
  9. Avoid retaining per-invocation content when aggregate counters are sufficient for the declared review functionality.
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
Findings (2)

Vague Triggers

Medium
Category
Not specified by scanner
Confidence
96% confidence
Finding

The trigger list is broad and overlaps with common user intents such as 'find new skills' and 'what skills are trending', which can cause the skill to activate in contexts the user did not intend. Because this skill performs autonomous network-based discovery and can later take community actions, accidental activation increases the chance of unneeded external requests and undesired workflow influence.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The skill states that reviews are drafted from 'real telemetry' and also performs automatic votes and flags, but it does not clearly disclose what usage data may be transmitted to LobstrHunt. This can lead to unintended sharing of behavioral metadata, task context, performance data, or other potentially sensitive operational information to a third party without sufficiently informed user consent.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.