PromptRiskDBThreat intelligence atlas

AI Agent Tool Invocation - AI Security Technique

AI Security Technique

Adversaries may use their access to an AI agent to invoke tools the agent has access to. LLMs are often connected to other services or resources via tools to increase their capabilities. Tools may include integrations with other applications, access to public or private data sources, and the ability to execute code. This may allow adversaries to execute API calls to integrated applications or services, providing t...

Overview

A source-backed snapshot of this AI security technique.

Adversaries may use their access to an AI agent to invoke tools the agent has access to. LLMs are often connected to other services or resources via tools to increase their capabilities. Tools may include integrations with other applications, access to public or private data sources, and the ability to execute code.

This may allow adversaries to execute API calls to integrated applications or services, providing the adversary with increased privileges on the system. Adversaries may take advantage of connected data sources to retrieve sensitive information. They may also use an LLM integrated with a command or script interpreter to execute arbitrary instructions.

AI agents may be configured to have access to tools that are not directly accessible by users. Adversaries may abuse this to gain access to tools they otherwise wouldn't be able to use.

Tactics2Attacker goals connected to this method.
Mitigations11Defenses that may help against this attack.
AI risks0Research-backed risks connected to this topic.

Technique details

Identifiers, maturity, and source taxonomy for this technique.

ATLAS ID
AML.T0053
Maturity
demonstrated
Priority score
193
ATLAS tactics
ExecutionPrivilege Escalation

Attack flow

How to read the public records connected to this technique.

1. TechniqueRead the ATLAS description and evidence level.
2. TacticsSee which attacker goals this method supports.
3. ExamplesCheck whether public case studies mention it.
4. DefensesReview safeguards mapped by ATLAS.
5. SourcesOpen the original public records and references.

Impact

Why this technique may deserve attention in the current dataset.

  • Evidence leveldemonstrated
  • Mapped defenses11 ATLAS mitigation records
  • Public examples14 linked case study records
  • Research risks0 related MIT AI Risk records above the confidence threshold
  • Vulnerabilities0 linked CVE records

Mitigations

Defenses that may help against this attack.

AML.M0028 - AI Agent Tools Permissions Configuration

Configuring AI Agent tools with access controls inherited from the user or the AI Agent invoking the tool can limit an adversary's capabilities within a system, including their ability to abuse tool invocations and access sensitive data.

LifecycleDeploymentCategoryTechnical - Cyber
Deployment

AML.M0024 - AI Telemetry Logging

Log AI agent tool invocations to detect malicious calls.

LifecycleDeployment + 1 moreCategoryTechnical - Cyber
DeploymentMonitoring

AML.M0020 - Generative AI Guardrails

Guardrails can prevent harmful inputs that can lead to plugin compromise, and they can detect PII in model outputs.

LifecycleML Model Engineering + 2 moreCategoryTechnical - ML
ML Model EngineeringML Model Evaluation+1 more

AML.M0021 - Generative AI Guidelines

Model guidelines can instruct the model to refuse a response to unsafe inputs.

LifecycleML Model Engineering + 2 moreCategoryTechnical - ML
ML Model EngineeringML Model Evaluation+1 more

Showing 4 of 10

Case studies

Examples from public reports and exercises.

OpenClaw Command & Control via Prompt Injection

Researchers at HiddenLayer demonstrated how a webpage can embed an indirect prompt injection that causes OpenClaw to silently execute a malicious script. Once executed, the script plants persistent malicious instructions into future system prompts, allowing the attacker to issue new commands, turning OpenClaw into a command and control agent.

What makes this attack unique is that, through a simple indirect prompt injection attack into an agentic lifecycle, untrusted content can be used to spoof the model’s control scheme and induce unapproved tool invocation for execution. Through this single inject, an LLM can become a persistent, automated command & control implant.

Date2026-02-03
exercise

Supply Chain Compromise via Poisoned ClawdBot Skill

A security researcher demonstrated a proof-of-concept supply chain attack using a poisoned ClawdBot Skill shared on ClawdHub, a Skill registry for agents. The poisoned Skill contained a prompt injection that caused ClawdBot to execute a shell command that reached the researcher's server. Although the researcher here used this access simply to warn users about the danger, they could have instead delivered a malicious payload and compromised the user's system. The security researcher recorded 16 different users who downloaded and executed the poisoned Skill in the first 8 hours of it being published on ClawdHub.

Date2026-01-26
exercise

Exposed ClawdBot Control Interfaces Leads to Credential Access and Execution

A security researcher identified hundreds of exposed ClawdBot control interfaces on the public internet. ClawdBot (now OpenClaw) “is a personal AI assistant you run on your own devices. It answers you on the channels you already use … , plus extension channels. … It can speak and listen on macOS/iOS/Android, and can render a live Canvas you control.”[1] The researcher was able to access credentials to a variety of connected applications via ClawdBot’s configuration file. They were also able to invoke ClawdBot’s skills by prompting it via the chat interface, leading to root access in the container.

The researcher searched Shodan[2] to identify Clawdbot instances exposed on the public internet, some without authentication enabled. The researcher demonstrated that the ClawdBot’s authentication mechanism could be bypassed due to a proxy misconfiguration.

With access to ClawdBot’s control interface, they were then able to access ClawdBot’s configuration, which contained credentials to a variety of other services. Across various exposed instances of ClawdBot, they identified Anthropic API Keys, Telegram Bot Tokens, Slack Oauth Credentials, and Signal Device Linking URIs. The researcher prompted ClawdBot directly via the chat interface, which led to exposure of its system prompt. They were also able to get ClawdBot to execute commands via it’s bash skill, which at least in once instance led to root access in the ClawdBot container.

The researcher noted a broad range of other impacts they could have had with this level of access, including:

  • Manipulation of user chat history with the ClawdBot AI agent
  • Exfiltration of conversation histories of any connected messaging services
  • Impersonation of users by sending messages on their behalf via connected messaging services

References

  1. [1] https://github.com/openclaw/openclaw
  2. [2] https://www.shodan.io/search?query=Clawdbot+Control
Date2026-01-25
exercise

Data Exfiltration via an MCP Server used by Cursor

The Backslash Security Research Team demonstrated that a Model Context Protocol (MCP) tool can be used as a vector for an indirect prompt injection attack on Cursor, potentially leading to the execution of malicious shell commands.

The Backslash Security Research Team created a proof-of-concept MCP server capable of scraping webpages. When a user asks Cursor to use the tool to scrape a site containing a malicious prompt, the prompt is injected into Cursor’s context. The prompt instructs Cursor to execute a shell command to exfiltrate the victim’s AI agent configuration files containing credentials. Cursor does prompt the user before executing the malicious command, potentially mitigating the attack.

Date2025-06-24
exercise

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Source evidence

Original public records and references for this page.