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A software supply chain attack on an AI agent skill happens when harmful instructions, code, or dependencies are introduced as a skill is created, distributed, installed, or used. Because skills can guide an agent in natural language and include executable scripts, a compromised skill may manipulate the agent, access data its permissions allow, or trigger unwanted actions.
Why a skill is part of the software supply chain
An agent skill is not necessarily just a prompt or a short description in a registry. It may combine natural-language instructions with scripts, dependencies, configuration, and other files. The agent may interpret those instructions and, depending on the host platform and permissions, run code or use connected tools. That combination makes a skill both content to evaluate and software to secure.
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The supply chain includes the people and systems that create, package, publish, approve, install, and execute a skill. A harmful change can enter at any of those points. The same concern extends beyond skill marketplaces: project instruction files and other shared repository context can also influence agent behavior.
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How an attack can move through the skill lifecycle
1. Creation: hide harmful behavior in the skill
An attacker can write deceptive or out-of-scope instructions, bundle scripts that do more than the advertised task, or add risky dependency actions. A skill may also imitate a familiar name or publisher. The mismatch between the skill’s stated purpose and its actual instructions or code is a key warning sign.
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Researchers have described two broad behavioral patterns: data thieves that seek to steal or exfiltrate information, and agent hijackers that attempt to steer the model’s decisions. These are categories observed in research, not outcomes that apply to every malicious skill.
2. Distribution: make the skill available to users
A malicious or altered package can be published through a community registry or shared another way. A plausible listing, familiar branding, or apparent popularity does not establish that the package contents are safe. Researchers have documented malicious samples in community skill ecosystems, but the studies do not establish the prevalence of malicious skills across every registry or platform.
3. Deployment: gain a foothold through installation or approval
When a user installs or enables a skill, the agent may be allowed to keep using it or to access tools and data relevant to the user’s work. An architecture analysis by Li and coauthors identifies persistent trust after a single approval as a structural risk: an approval made at installation may have effects beyond the moment it is granted.
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When the agent uses the skill, it may follow the skill’s instructions, run included code, or make tool calls. What a compromised skill can actually do depends on the host agent’s design, the user’s permissions, and the connected tools. Possible consequences include reading accessible files or credentials, sending data outside the environment, changing project state, making unauthorized tool calls, or running unwanted commands.
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5. Propagation: carry instructions into later work
Instructions can persist or spread when memory, configuration, project files, or multi-agent workflows carry context into subsequent sessions or other agents. This means a review limited to a marketplace package can miss harmful instructions introduced through the repository or workflow in which the skill is used.
What published studies found—and what their numbers mean
Several 2026 studies report confirmed malicious skills or security issues in particular samples. Their populations and methods differ, so their figures should not be combined into a single estimate or treated as a current rate for all agent skills.
| Study | Reported scope and findings | How to interpret it |
|---|---|---|
| Yi Liu and coauthors, 2026, “Do Not Mention This to the User”: Detecting and Understanding Malicious Agent Skills | Examined 98,380 agent skills across two community registries; reported 157 confirmed malicious skills and 632 vulnerabilities. The paper reports a median of three kill-chain phases per malicious skill and an average of 4.03 vulnerabilities. It attributes 54.1% of confirmed cases to one actor using templated brand impersonation. | These figures describe the study’s dataset and methods, not all registries or malicious-skill activity. The actor share applies to the collected confirmed cases. |
| Beurer-Kellner and coauthors, 2026 | Analyzed 3,984 agent skills; reported 76 confirmed malicious payloads and found that 13.4% had at least one critical-level security issue. | Confirmed malicious payloads and the broader critical-issue category are different measures. Do not compare its rate directly with the other study’s results. |
| Li and coauthors, 2026, architecture analysis | Reports five confirmed incidents and organizes its threat taxonomy into seven categories and seventeen scenarios. | The incident count and taxonomy describe the paper’s scope; they are not a count of every incident in the ecosystem. |
Liu and coauthors also report that 93.6% of the malicious skills they identified were removed within 30 days following responsible disclosure. That is the outcome in their study, not a guarantee that a registry will remove a reported skill within that period.
What to review before trusting a skill
Use a review that covers the skill’s claims, complete contents, origin, and behavior—not only its registry summary or executable files.
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- Limit where skills can come from. Prefer approved registries and publishers, and require internal approval before using a skill in production.
- Inspect the complete package. Read the natural-language instructions as well as scripts, dependencies, and configuration. Check whether each action is necessary for the advertised task.
- Verify origin and integrity. Check provenance and available hashes, and detect changes between review and installation. A publisher name or listing alone is not proof that the package reviewed is the package installed.
- Review surrounding context. Include skill, agent, and project instruction files in repository review. A Cloud Security Alliance rapid-research note discusses malicious project configuration and context files, as well as hidden Unicode instruction injection. The note says it was AI-assisted and did not undergo CSA’s official review and approval; treat it as a qualified practitioner note, not an official CSA standard.
- Keep access narrow. Give the agent only the file, tool, and network permissions needed for the task. Isolate sensitive work where practical and restrict outbound network access.
- Monitor actual behavior. Log tool invocations, filesystem writes, and outbound connections. Investigate unexpected destinations, secret-like values in outbound requests, or actions outside the skill’s declared purpose.
- Keep the platform current. Apply current updates to the agent platform and the software it relies on.
Where the platform supports it, filtering unexpected Unicode character classes before instructions reach the model may help with hidden-character risks. This measure is discussed in the CSA rapid-research note described above.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate a scanner or organizational control
No single static scan can establish that a skill will behave safely at runtime. When evaluating a security control or procurement option, look for coverage across the different stages of the risk:
- Instruction analysis: Does it inspect natural-language instructions for deception or out-of-scope behavior?
- Code and dependency review: Does it scan scripts and dependencies as well as descriptive text?
- Provenance and tamper detection: Can it establish where content came from and identify changes after approval?
- Runtime visibility: Does it observe agent tool calls, file changes, and network activity?
- Permission and egress controls: Can it restrict what the agent can access and where it can send data?
- Workflow fit: Can it support developer workflows without turning registry availability or a scan result into a substitute for security review?
These controls address different failure points. For example, provenance can help detect an unexpected package change but does not show whether a reviewed instruction is manipulative; runtime monitoring can expose unexpected behavior but does not make excessive permissions safe.
What the evidence does not establish
The cited empirical studies examine specific samples, registries, and methods. Their findings demonstrate that malicious and vulnerable agent skills exist, but they do not provide a stable prevalence estimate for every marketplace, framework, or current catalog. Registry contents and platform mitigations can also change over time. Treat each published count as a result for the study that produced it, rather than as a universal risk percentage.
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