PromptRiskDBThreat intelligence atlas

Indirect - AI Security Technique

AI Security Technique

An adversary may inject prompts indirectly via separate data channel ingested by the LLM such as include text or multimedia pulled from databases or websites. These malicious prompts may be hidden or obfuscated from the user. This type of injection may be used by the adversary to gain a foothold in the system or to target an unwitting user of the system.

Overview

A source-backed snapshot of this AI security technique.

Tactics0Attacker goals connected to this method.
Mitigations2Defenses 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.T0051.001
Maturity
demonstrated
Priority score
156

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 defenses2 ATLAS mitigation records
  • Public examples13 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.M0024 - AI Telemetry Logging

Telemetry logging can help identify if unsafe prompts have been submitted to the LLM.

LifecycleDeployment + 1 moreCategoryTechnical - Cyber
DeploymentMonitoring

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

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

Living Off AI: Prompt Injection via Jira Service Management

Researchers from Cato Networks demonstrated how adversaries can exploit AI-powered systems embedded in enterprise workflows to execute malicious actions with elevated privileges. This is achieved by crafting malicious inputs from external users such as support tickets that are later processed by internal users or automated systems using AI agents. These AI agents, operating with internal context and trust, may interpret and execute the malicious instructions, leading to unauthorized actions such as data exfiltration, privilege escalation, or system manipulation.

Date2025-06-19
exercise

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Source evidence

Original public records and references for this page.