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AI Exposure Incident Monitor

Job: 7-Day Signal Validation for AI Execution Exposure Incident Feed​

Objective​

We want to test whether there is enough publicly observable signal to support a weekly incident series that highlights AI systems holding or exposing execution authority across APIs, SaaS platforms, or enterprise systems.

The purpose is not to build a threat-intel product.

The purpose is to determine whether we can consistently surface real-world evidence supporting a key hypothesis:

AI agents, automations, and LLM-powered workflows are already being deployed with meaningful execution authority, often with weak governance.

If such incidents appear regularly in public sources, they can be translated into short security observations that resonate with CISOs and security leaders.

The output would later be used for thought leadership and demand generation, not monetization.

This 7-day exercise is purely a signal validation experiment.

Hypothesis Being Tested

We are testing the following assumption:

There are enough publicly observable artifacts related to AI agents, LLM workflows, or AI integrations with execution authority to support at least one strong, credible incident post per week.

These incidents must demonstrate something meaningful about execution authority risk, such as:

  • AI agents interacting with enterprise APIs
  • exposed LLM API keys tied to workflows
  • agent frameworks configured with external actions
  • integrations between LLMs and SaaS systems
  • leaked prompts, configs, or automation scripts
  • exposed vector databases or RAG pipelines
  • AI plugins with elevated privileges
  • compromised or exposed automation tokens tied to AI workflows

We are not looking for generic AI security stories.

We are specifically looking for evidence of AI systems performing actions or having authority to perform actions.

Why This Matters

Security buyers (CISOs) rarely act on theoretical risks.

They act when they see observable evidence that a problem already exists in the wild.

If we can consistently surface incidents showing:

  • AI agents with standing execution authority
  • poorly scoped AI integrations
  • exposed AI credentials tied to automation
  • AI workflows connected to sensitive systems

Then those incidents can be translated into short observations like:

“This agent had the ability to read/write CRM records through an LLM integration.”

Those observations help start conversations with security leaders.

However, this only works if credible signals appear frequently enough.

If incidents are too rare or too weak, the concept does not work.

This experiment determines whether the signal exists.

Scope of the Experiment

This is a rapid validation exercise, not a production system.

The system should collect signals from a limited number of high-value sources and produce a daily digest of candidate incidents.

Focus on sources that are likely to surface early signals:

Primary sources​

GitHub public repositories

Search for:

  • exposed LLM API keys
  • agent frameworks (LangChain, CrewAI, AutoGPT, Semantic Kernel, AutoGen)
  • agents calling APIs or SaaS platforms
  • automation scripts using LLM tools
  • prompt files and tool definitions
  • RAG pipelines and vector DB setups

GitHub is likely the highest signal source.

Security research feeds

RSS feeds from:

  • security researchers
  • vulnerability research blogs
  • AI security research
  • incident writeups

These sometimes include:

  • exposed AI plugins
  • prompt injection exploits
  • AI integrations exposing data

CVE / vulnerability feeds

Track vulnerabilities involving:

  • LLM platforms
  • AI plugins
  • vector databases
  • model gateways
  • agent frameworks

Optional sources​

These can be included but are lower priority:

  • Paste sites
  • developer forums
  • vulnerability disclosure databases
  • security newsletters

Do not spend time on dark web scraping during this test.

It adds complexity without improving validation.

What the System Should Do

The system should perform a daily scan of the defined sources and generate candidate signals.

Each candidate should include:

  • source
  • title or description
  • link
  • date discovered
  • raw snippet or summary

Then evaluate each candidate using three scoring dimensions.

Scoring Criteria

Each candidate should be scored 1–5 on the following dimensions.

AI Relevance​

How clearly is this incident related to AI systems?

Examples of high scores:

  • LLM API key exposed
  • agent framework repository
  • LLM tool-calling integration
  • AI plugin vulnerability

Low scores:

  • generic API key leaks
  • unrelated credentials

Execution Authority Relevance​

Does the AI system have the ability to perform actions?

Examples:

  • agent calling external APIs
  • LLM connected to SaaS platform
  • automation workflow triggered by AI
  • plugin capable of modifying data

Higher scores indicate actual operational authority, not just AI usage.

Post Worthiness​

Can this signal realistically be turned into a short, credible observation for a security audience?

Example:

Weak:

“Someone leaked an OpenAI key.”

Strong:

“Public repo exposes LangChain agent capable of querying Salesforce using stored credentials.”

Only strong signals matter.

Daily Output

Each run should produce a markdown report containing:

Candidate Signals​

List all discovered candidates with scores and explanations.

High Potential Incidents​

Filtered list where:

AI relevance ≥ 4

Execution relevance ≥ 4

Post-worthiness ≥ 4

These represent possible weekly incident posts.

Draft Observation​

For the strongest candidates, generate:

  • one short incident summary
  • what authority was present
  • why it matters for security teams
  • a potential LinkedIn-style observation

Success Criteria

After 7 days we will evaluate:

Raw signal volume

Minimum expectation:

10–20 candidate signals discovered.

Strong incident candidates

Minimum expectation:

At least 2–3 strong incidents that could plausibly support a LinkedIn post.

This suggests we can sustain 1 incident per week.

Narrative strength

At least one incident should clearly demonstrate:

AI system + execution authority + security implication.

If incidents are weak or ambiguous, the concept fails.

Failure Conditions

The experiment should be considered unsuccessful if:

  • fewer than 5 relevant signals appear during the week
  • signals are mostly generic API key leaks
  • incidents cannot be tied to execution authority
  • incidents cannot be explained clearly to a CISO audience
  • the system surfaces mostly irrelevant noise

In that case, the signal is likely too weak to support a weekly incident series.

Constraints

Keep implementation extremely simple.

Requirements:

  • Python implementation
  • GitHub API usage for repository search
  • RSS feed ingestion
  • CVE feed ingestion
  • SQLite or CSV storage
  • simple deduplication
  • configurable keyword list

Do not build a large system.

This is a validation experiment only.

Final Deliverables

At the end of the 7-day run, provide:

  1. The collected dataset
  2. The strongest incident examples
  3. A short assessment:
  • Is there enough signal for weekly incidents?
  • Which sources produced the best signals?
  • What types of incidents appear most often?

The final output should allow us to confidently decide: Proceed with the concept or abandon it.