HiddenLayer researchers have developed the first single, universal prompt injection technique, post-instruction hierarchy, that successfully bypasses safety guardrails across nearly all major frontier AI models. This includes models from OpenAI (GPT-4o, GPT-4o-mini, and even the newly announced GPT-4.1), Google (Gemini 1.5, 2.0, and 2.5), Microsoft (Copilot), Anthropic (Claude 3.7 and 3.5), Meta (Llama 3 and 4 families), DeepSeek (V3, R1), Qwen (2.5 72B), and Mixtral (8x22B).
The technique, dubbed Prompt Puppetry, leverages a novel combination of roleplay and internally developed policy techniques to circumvent model alignment, producing outputs that violate safety policies, including detailed instructions on CBRN threats, mass violence, and system prompt leakage. The technique is not model-specific and appears transferable across architectures and alignment approaches.
The research provides technical details on the bypass methodology, real-world implications for AI safety and risk management, and the importance of proactive security testing, especially for organizations deploying or integrating LLMs in sensitive environments.
Threat actors now have a point-and-shoot approach that works against any underlying model, even if they do not know what it is. Anyone with a keyboard can now ask how to enrich uranium, create anthrax, or otherwise have complete control over any model. This threat shows that LLMs cannot truly self-monitor for dangerous content and reinforces the need for additional security tools.
Is it Patchable?
It would be extremely difficult for AI developers to properly mitigate this issue. That’s because the vulnerability is rooted deep in the model’s training data, and isn’t as easy to fix as a simple code flaw. Developers typically have two unappealing options:
- Re-tune the model with additional reinforcement learning (RLHF) in an attempt to suppress this specific behavior. However, this often results in a “whack-a-mole” effect. Suppressing one trick just opens the door for another and can unintentionally degrade model performance on legitimate tasks.
- Try to filter out this kind of data from training sets, which has proven infeasible for other types of undesirable content. These filtering efforts are rarely comprehensive, and similar behaviors often persist.
That’s why external monitoring and response systems like HiddenLayer’s AISec Platform are critical. Our solution doesn’t rely on retraining or patching the model itself. Instead, it continuously monitors for signs of malicious input manipulation or suspicious model behavior, enabling rapid detection and response even as attacker techniques evolve.
Impacting All Industries
In domains like healthcare, this could result in chatbot assistants providing medical advice that they shouldn’t, exposing private patient data, or invoking medical agent functionality that shouldn’t be exposed.
In finance, AI analysis of investment documentation or public data sources like social media could result in incorrect financial advice or transactions that shouldn’t be approved as well as utilize chatbots to expose sensitive customer financial data & PII.
In manufacturing, the greatest fear isn’t always a cyberattack but downtime. Every minute of halted production directly impacts output, reduces revenue, and can drive up product costs. AI is increasingly being adopted to optimize manufacturing output and reduce those costs. However, if those AI models are compromised or produce inaccurate outputs, the result could be significant: lost yield, increased operational costs, or even the exposure of proprietary designs or process IP.
Increasingly, airlines are utilizing AI to improve maintenance and provide crucial guidance to mechanics to ensure maximized safety. If compromised, and misinformation is provided, faulty maintenance could occur, jeopardizing
public safety.
In all industries, this could result in embarrassing customer chatbot discussions about competitors, transcripts of customer service chatbots acting with harm toward protected classes, or even misappropriation of public-facing AI systems to further CBRN (Chemical, Biological, Radiological, and Nuclear), mass violence, and self-harm.
AI Security has Arrived
Inside HiddenLayer’s AISec Platform and AIDR: The Defense System AI Has Been Waiting For
While model developers scramble to contain vulnerabilities at the root of LLMs, the threat landscape continues to evolve at breakneck speed. The discovery of Prompt Puppetry proves a sobering truth: alignment alone isn’t enough. Guardrails can be jumped. Policies can be ignored. HiddenLayer’s AISec Platform, powered by AIDR—AI Detection & Response—was built for this moment, offering intelligent, continuous oversight that detects prompt injections, jailbreaks, model evasion techniques, and anomalous behavior before it causes harm. In highly regulated sectors like finance and healthcare, a single successful injection could lead to catastrophic consequences, from leaked sensitive data to compromised model outputs. That’s why industry leaders are adopting HiddenLayer as a core component of their security stack, ensuring their AI systems stay secure, monitored, and resilient.
