Engineer AI for Failure: Contain Prompt Injection

Read Articleadded Sep 26, 2025

LLMs have an inherent security flaw because they can’t separate code from data, making them vulnerable to prompt injections. These attacks can be trivial or seriously damaging, especially when models are connected to tools or sensitive systems. The article urges a safety-engineering approach—interlocks, isolation, least privilege, and verification—to contain failures and prevent harm.

Key Points

  • LLMs blur the line between code and data, creating an inherent vulnerability.
  • Prompt injections can trick models into following instructions they should ignore, with consequences ranging from trivial to dangerous.
  • Developers should adopt a mechanical/safety-engineering mindset: design for failure, containment, and verification.
  • Mitigations include isolation, least-privilege access, sandboxed tool use, audited interfaces, and defense-in-depth.

Sentiment

Overall, the sentiment of the Hacker News discussion is mixed but leans towards agreement with the article's identification of a critical security problem within LLMs. While most acknowledge the severity of prompt injection and the need for robust security, there's significant disagreement and critique regarding the article's analogies, its specific explanations of LLM behavior, and the novelty of its proposed solutions. Many commenters contribute alternative perspectives, highlight practical difficulties in mitigation, or suggest that the AI industry is simply neglecting existing security principles.

In Agreement

  • The "lethal trifecta" (code-data blur, prompt injection, consequential actions) accurately identifies a critical and severe security flaw in LLMs.
  • Prompt injection is a nasty, fundamental threat that requires robust mitigation strategies.
  • AI engineers need to adopt a more rigorous, safety-engineering mindset, moving beyond the assumption that more training data or better prompts will solve security issues.
  • The consequences of data breaches, enabled by prompt injection, can indeed be "lethal" in terms of financial ruin, severe legal penalties, and risks to national security.
  • Solutions like simple "guardrails" or filters designed to detect and block potential attacks are insufficient and likely create a false sense of security.
  • Applying defense-in-depth principles, such as sandboxing, minimal permissions, and strictly mediating tool use, is crucial for containing failures and reducing risks.

Opposed

  • The article's analogy comparing LLM security to bridge-building is flawed because physical structures do not contend with adversarial attacks, and software has unique challenges like mutability and infinite dependencies.
  • LLMs are technically deterministic but unpredictable or chaotic, rather than truly non-deterministic, making the article's argument based on non-determinism a 'non-sequitur' in the security context.
  • The article's suggested solutions are merely 'security 101' and not novel; the problem stems from the AI industry's 'careless mindset' and neglect of long-established software security best practices.
  • Cutting off one of the 'legs' of the lethal trifecta (e.g., exfiltration) is significantly harder than implied, as LLMs can social engineer users to bypass controls, and the 'legs' themselves are interrelated (e.g., untrusted content can become private data).
  • The problem of distinguishing code from data in LLMs might be a fundamental, computationally impossible issue, reminiscent of problems like undecidability, making a complete solution unattainable.
  • While data breaches are serious, labeling them 'lethal' downplays other, more directly life-threatening AI failure modes.