A serious new threat is emerging in the AI space, and it’s called Policy Puppetry. According to cybersecurity firm HiddenLayer, this novel prompt injection technique can bypass safety measures across nearly every major generative AI platform—including models from OpenAI, Google, Anthropic, Meta, Microsoft, and more.
While these large language models (LLMs) are built with multiple layers of safety training to prevent the generation of dangerous content, Policy Puppetry has found a way around them. And the implications are alarming.
How Policy Puppetry Bypasses Safety Alignments
At its core, the attack works by disguising malicious prompts as benign policy files—think XML, JSON, or INI formats. When a model encounters one of these prompts, it treats it as internal policy logic instead of user-generated input. This clever trick causes the LLM to override its safety instructions and alignment protocols.
What makes this even more dangerous is that the technique doesn’t depend on a specific policy language. It simply reframes the prompt to look like structured config data. Once the model interprets the prompt as a policy file, attackers can inject additional directives that reshape output behaviors—turning off safety switches without the model realizing it.
This isn’t a one-off flaw or a glitch. HiddenLayer successfully tested Policy Puppetry across a wide array of leading gen-AI models, including those developed by:
- OpenAI
- Microsoft
- Meta
- Anthropic
- Mistral
- DeepSeek
- Qwen
In every case, the method proved effective, sometimes requiring only minimal adjustments to succeed.
Why It’s a Game-Changer for AI Security
Generative AI models are trained to avoid producing harmful responses to queries related to violence, CBRN threats (chemical, biological, radiological, nuclear), or self-harm—even in roundabout or fictional scenarios. Yet techniques like Policy Puppetry and earlier jailbreaks such as Context Compliance Attacks (CCA) or narrative engineering show that AI safety is still fragile.
HiddenLayer’s discovery is particularly troubling because it is the first known attack that can bypass instruction hierarchies across nearly all LLMs. That means these models—despite sophisticated reinforcement learning and alignment layers—can still be manipulated into delivering restricted or harmful outputs.
Worse, once a method like this is discovered, it becomes easy for malicious actors to replicate and scale it. With tools like Policy Puppetry now in circulation, the threshold for launching prompt-based attacks on AI systems is much lower.
What This Means for AI Developers and Enterprises
The big takeaway? AI systems cannot be relied upon to self-regulate dangerous content. Even with advanced training, models remain vulnerable to cleverly engineered inputs that exploit their own internal logic.
As HiddenLayer warns, defending against such attacks will require more than prompt tuning. Developers and AI security teams must implement external safety controls, robust content filtering, and real-time anomaly detection layers to protect users and prevent misuse.
The emergence of Policy Puppetry is a wake-up call for the entire industry—highlighting the fundamental limitations in how today’s AI systems are trained and aligned, and underscoring the urgent need for more comprehensive AI security tools.
