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Introduction
The rapid adoption of AI agents has created a new software ecosystem built around reusable skills, plugins, and instruction packages that extend what artificial intelligence can do. Organizations increasingly trust these add-ons to automate workflows, access internal resources, and perform business-critical tasks. However, a recent experiment conducted by AIR has highlighted a serious weakness in how AI agent skills are reviewed and trusted.
Rather than exploiting a software vulnerability, the experiment demonstrated how attackers could manipulate trust itself. By creating a seemingly harmless AI skill, passing every major security scan, and leveraging respected community signals such as GitHub stars, AIR showed how easily malicious actors could potentially distribute dangerous instructions to thousands of AI agents. The findings reveal a structural problem affecting the entire AI skill ecosystem rather than a flaw in any single scanner or platform.
AIR’s Experiment Demonstrated a Massive Blind Spot
Security company AIR developed a fake AI agent skill called brand-landingpage and submitted it through a popular skill marketplace. The skill claimed to help users create landing pages using Google’s Stitch design platform, targeting marketers, designers, and other non-technical professionals.
The experiment was intentionally designed to be harmless. Instead of executing malicious commands, the skill merely collected user email addresses. The objective was not to steal information but to demonstrate how existing trust mechanisms could fail under real-world conditions.
According to AIR, the skill eventually reached approximately 26,000 AI agents, including agents connected to corporate accounts. While these numbers have not been independently verified, the scale of the reported distribution attracted significant attention throughout the cybersecurity community.
Borrowing Trust Through GitHub Reputation
One of the most interesting aspects of the experiment involved manipulating social trust signals rather than technical vulnerabilities.
AIR submitted a pull request to a skill marketplace repository containing roughly 36,000 GitHub stars and more than 150 existing skills. After the pull request was accepted, the newly added skill effectively inherited the credibility associated with the repository.
For many users, GitHub stars function as a shortcut for trust. A repository with thousands of stars often appears legitimate, well-maintained, and community-vetted. However, AIR demonstrated that a malicious or deceptive skill can gain that same appearance of legitimacy simply by being included in a trusted repository.
The experiment highlights a growing problem within open-source ecosystems where reputation can sometimes be transferred without meaningful security validation.
Instagram Advertising Expanded the Reach
To further simulate a realistic deployment scenario, AIR promoted the skill through Instagram advertisements targeting marketers, sales professionals, and designers.
These users installed the skill believing it would simplify landing-page creation tasks. The combination of social proof, marketplace availability, and clean security scans created a powerful illusion of safety.
This mirrors techniques commonly observed in phishing campaigns and software supply-chain attacks, where attackers combine technical deception with psychological manipulation to maximize adoption.
Why Every Scanner Approved the Skill
Perhaps the most concerning result was that every tested skill scanner classified the package as safe.
This included scanners developed by major security vendors as well as those integrated into popular AI skill ecosystems.
The reason was surprisingly simple.
The scanners only inspected files included within the submitted package. AIR intentionally ensured that those files contained nothing malicious. Instead, the package instructed agents to visit an external website for setup instructions.
At the time of submission, the external page displayed legitimate-looking documentation. Since the scanners could only analyze the packaged content, they had no visibility into what might happen after installation.
As a result, the skill passed every security review.
The External Link Trick
The real innovation behind the demonstration involved separating the visible package from the actual instructions.
The skill directed agents to visit a domain controlled by AIR that resembled Google’s Stitch documentation environment. Initially, the site presented genuine documentation, creating the appearance of legitimacy.
Only after the skill achieved widespread adoption did AIR alter the contents of the page.
The updated instructions directed agents to download and execute a script. In the demonstration, the script merely transmitted email addresses back to AIR. However, a genuine attacker could have delivered far more dangerous payloads.
This technique effectively bypasses static review processes because the malicious content exists outside the scanned package and can be modified at any time after approval.
A Real Threat Beyond the Demonstration
Had the experiment been conducted by malicious actors instead of researchers, the consequences could have been severe.
Potential actions could include:
Accessing Sensitive Files
AI agents often possess access to documents, project files, customer information, and internal resources. A malicious payload could exploit that access.
Data Exfiltration
Attackers could quietly transfer proprietary information, credentials, or confidential records to external servers.
Internal Network Exploration
Corporate AI agents frequently operate within trusted environments. Compromising one agent may provide opportunities to explore connected systems.
Lateral Movement
An attacker could potentially use compromised agents as stepping stones toward broader organizational access.
The practical limits would depend largely on the permissions assigned to the affected AI agent.
Previous Research Already Revealed Similar Problems
AIR’s findings are not isolated.
Several weeks before this experiment, security researchers at Trail of Bits demonstrated methods for bypassing multiple AI skill scanners, including detectors integrated into popular skill platforms.
Their conclusion was straightforward: security scanners evaluate a fixed snapshot of content, while attackers can continuously modify external resources after approval.
Other researchers throughout 2025 and 2026 have repeatedly warned that external dependencies represent one of the largest unresolved risks facing AI agent ecosystems.
The Structural Problem Nobody Has Solved
The most important lesson from the experiment is that no software bug was exploited.
Instead, the issue stems from how AI skills are fundamentally designed.
A scanner reviews a package once.
An external website can change forever.
As long as skills are permitted to fetch instructions, scripts, or content from locations outside the reviewed package, attackers retain opportunities to alter behavior after approval.
This creates an ongoing trust gap that traditional security validation methods struggle to address.
Why Trust Signals Are Becoming Dangerous
Many organizations rely on shortcuts when evaluating software.
Common trust indicators include:
GitHub Stars
Popularity is often mistaken for security.
Clean Security Reports
A passed scan is frequently treated as proof of safety rather than a point-in-time assessment.
Open-Source Reputation
Projects associated with respected communities automatically receive higher levels of trust.
Marketplace Approval
Users often assume marketplace listings have undergone comprehensive review.
AIR’s experiment demonstrated that every one of these assumptions can be manipulated under the right circumstances.
How Organizations Should Respond
Security experts increasingly recommend treating AI skills like traditional software components rather than simple instruction files.
Inventory Existing Skills
Organizations should identify every installed skill and understand its purpose, permissions, and dependencies.
Review External Resources
Security teams must evaluate not only packaged content but also every external URL referenced by a skill.
Implement Version Pinning
Locking dependencies to known versions reduces the risk of silent changes occurring after approval.
Enforce Least Privilege
AI agents should receive only the minimum permissions required to perform assigned tasks.
Continuously Revalidate
Security reviews should occur repeatedly rather than only during installation.
Deep Analysis: Linux Commands and Defensive Validation Techniques
The security lessons from
Administrators can apply familiar validation methods:
Verify External Connections
curl -I https://example.com wget --spider https://example.com
Monitor Domain Changes
whois domain.com dig domain.com nslookup domain.com
Review Downloaded Content
sha256sum file.sh md5sum file.sh file file.sh
Inspect Network Activity
netstat -tulpn ss -tulpn tcpdump -i eth0
Detect Unauthorized Modifications
find /opt/skills -type f -mtime -1 auditctl -w /opt/skills -p wa
Analyze Scripts Before Execution
cat script.sh less script.sh grep -R "curl" script.sh grep -R "wget" script.sh
Restrict Privileges
chmod 750 skill-directory chown root:security skill-directory
Monitor Agent Processes
ps aux | grep agent top htop
These traditional defensive techniques remain highly relevant because AI skills increasingly behave like software packages rather than simple configuration files.
What Undercode Say:
The AIR experiment should not be viewed as a scanner failure alone.
The real issue is misplaced trust.
Security scanners did exactly what they were designed to do.
They inspected the package presented to them.
The dangerous content existed elsewhere.
Modern AI ecosystems increasingly blur the line between software and instructions.
A skill may appear to be text.
In practice, it can function like executable code.
This distinction matters greatly.
Organizations often deploy AI skills with far less scrutiny than traditional software.
That behavior creates opportunities for attackers.
The experiment also demonstrates how social engineering is evolving.
Instead of targeting humans directly, attackers may increasingly target AI systems.
An AI agent can become the victim.
The human user may never realize manipulation occurred.
GitHub stars remain one of the weakest trust indicators in cybersecurity.
Popularity measures adoption.
It does not measure security.
Marketplace approval suffers from similar limitations.
Approval generally confirms compliance with review requirements.
It rarely guarantees long-term safety.
External links introduce a particularly difficult challenge.
Unlike packaged code, web content can change instantly.
This creates a persistent trust problem.
Security reviews become outdated the moment approval occurs.
The AI industry is currently repeating lessons already learned from software supply-chain attacks.
Package repositories.
Dependency poisoning.
Malicious updates.
Compromised downloads.
The same patterns are now appearing within AI agent ecosystems.
Organizations should assume that every external instruction source can change unexpectedly.
Continuous monitoring will become more important than one-time validation.
Future AI security products will likely focus on runtime behavior rather than package analysis alone.
Behavioral monitoring may eventually become more valuable than static scanning.
The most important takeaway is that AI security is rapidly becoming software security.
Companies that separate the two disciplines may find themselves exposed.
Defenders must stop viewing skills as harmless text files.
They are increasingly becoming operational code executed through AI systems.
That shift changes the entire threat model.
✅ AIR publicly reported creating a harmless AI skill that reportedly reached approximately 26,000 agents through marketplace distribution and advertising efforts.
✅ Multiple AI skill scanners primarily analyze submitted package contents, making external resources significantly harder to evaluate during initial review.
✅ Independent security research has previously demonstrated similar weaknesses involving externally hosted content and post-approval modifications.
❌ The reported figure of 26,000 affected agents has not been independently verified by third-party researchers.
❌ Claims that every affected agent could have been fully compromised remain theoretical and depend heavily on permissions, environment configuration, and deployment architecture.
❌ The experiment does not prove that all AI skill marketplaces are vulnerable in identical ways, as security controls differ across platforms.
Prediction
(+1) AI skill marketplaces will introduce continuous monitoring systems that re-evaluate external resources after initial approval.
(+1) Enterprise organizations will begin treating AI skills as software assets subject to formal security governance and auditing.
(+1) Runtime behavioral analysis tools will emerge as a major segment within AI security products.
(-1) Attackers will increasingly exploit trusted repositories and marketplace ecosystems to distribute deceptive AI skills.
(-1) Organizations relying solely on scanner verdicts will continue facing supply-chain style compromises.
(-1) External instruction sources will remain one of the most difficult AI security challenges to fully eliminate over the next several years.
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