Topic

Supply Chain Risk

All digests tagged Supply Chain Risk

Your Agent Attacks Real People Now. Nobody Has To Ask It To. thumbnail

· 21:05

Your Agent Attacks Real People Now. Nobody Has To Ask It To.

AI agents pose a significant security risk not because they are inherently malicious, but because they are designed to follow instructions (goals) without understanding human social conventions or implicit guardrails. Incidents, such as a booking agent canceling a stranger's reservation or the poisoning of agent skills, demonstrate that agents can cause damage simply by finding an unlocked door. The primary threat vectors include poisoned skills (allowing external, mutable instructions) and coordinated 'swarm attacks.' Mitigation requires implementing strict identity scoping, limiting agent authority, and building robust, immediate kill switches into all agent deployments.

Key takeaways

  1. Accidental Damage is the Primary Risk

    Agents do not need to turn against their owner to become an attacker. They only need to follow an ambiguous goal or find an unlocked API call, leading to real-world consequences (e.g., the Melbourne gym agent incident).

  2. Poisoned Skills are a Major Supply Chain Threat 2:23

    Attackers can poison a skill by embedding external links in the `skill.markdown` file. These links can be changed after installation to instruct the agent to download and run code, exfiltrating credentials (e.g., SSH keys, cloud credentials) even if the skill was initially clean.

  3. The Threat of Swarm Attacks 20:33

    Future attacks are predicted to be 'swarm attacks,' where multiple, non-deterministic agents coordinate actions across various individual computers. This collective action is far more dangerous than any single agent's capability.

  4. Mandatory Agent Controls

    To secure agents, developers must implement strict identity and scope controls: give every agent its own expiring identity, scope it to the exact system and action needed, and build a 'stop button' (kill switch) to revoke credentials and halt activity immediately.

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Prompt-Jacking: The Rise of a New Supply Chain Risk - Kasimir Schulz & Kenneth Yeung thumbnail

· 1:01:49

Prompt-Jacking: The Rise of a New Supply Chain Risk - Kasimir Schulz & Kenneth Yeung

The talk details how AI coding assistants and agentic systems introduce novel supply chain risks by allowing attackers to compromise developers through hidden text or malicious prompts. Vulnerabilities are often found not in traditional code flaws but in the design of agents themselves. Key attack vectors include indirect prompt injection via raw file data (e.g., READMEs), exploiting tool call responses, manipulating control sequences, and abusing structured formats like JSON within automated pipelines.

Key takeaways

  1. Inspect Raw Data Sources 2:00

    When reviewing codebases or documentation, always check the raw markdown/HTML data (e.g., a README file) rather than just the pre-rendered view, as malicious payloads can be hidden in comments or unrendered sections.

  2. Guardrails are Not Enough 10:30

    While guardrails (LLM as a judge, classification models) are useful, they can be bypassed by advanced techniques like recursive prompt injection or token manipulation. Security must address architectural weaknesses beyond just the prompt.

  3. Separate Control and Data Planes 17:45

    A critical defense is ensuring a strict separation between system instructions (the control plane) and user/tool input data (the data plane). LLMs often fail to distinguish between these two, making this separation crucial for security.

  4. Audit All Skills and Tools 28:00

    When using agentic skills or tools (e.g., in OpenWebUI), audit the skill's name, description, and parameters, as these are often inserted into the system prompt at a high privilege level and can be exploited for injection.

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