Universal LLM Jailbreak Exposes ChatGPT, Gemini, Claude, and More: Policy Puppetry Attack and Security Implications
- Nox90 Engineering
- 6 days ago
- 8 min read

Universal LLM Jailbreak: The Policy Puppetry Attack and Its Security Implications
By Nox90 Senior Technology Analyst, June 2025
Executive Summary
In early 2025, a novel and deeply concerning vulnerability was uncovered in the security architecture of all major large language models (LLMs) in use today. The "Policy Puppetry" attack, revealed by HiddenLayer and corroborated by multiple independent sources, enables adversaries to universally bypass LLM safety guardrails using a single prompt template—regardless of vendor, architecture, or training pipeline.
This report provides a comprehensive, security-focused analysis of the Policy Puppetry attack. We examine the technical details and underlying model weaknesses, review the implications for both attackers and defenders, and outline best practices for developing and deploying LLM-powered applications securely. The discovery of this universal jailbreak is a watershed moment, demonstrating that current alignment and reinforcement learning techniques, previously assumed robust, are insufficient as standalone defenses.
For organizations leveraging LLMs in any capacity—customer service, healthcare, finance, or critical infrastructure—the risks are immediate and significant. Malicious actors can now more easily generate harmful content, extract confidential system prompts, and manipulate AI agents. For defenders, a shift toward layered security, continuous adversarial testing, and rapid incident response is now essential.
Nox90 stands ready to help clients understand, mitigate, and manage these new AI risks by integrating secure software development practices and advanced application security solutions.
Table of Contents
- Introduction
- The Policy Puppetry Attack: A Universal Jailbreak
- Background: The Promise and Peril of LLM Alignment
- Discovery: A Single Prompt to Rule Them All
- Technical Details: Anatomy of Policy Puppetry
- Key Innovations and Differentiators
- Universality and Transferability
- Exploitation of Instruction Hierarchy
- Fictional Framing and Encoding
- System Prompt Extraction
- Expert and Community Perspectives
- What Does It Mean from a Cyber Perspective?
- Attackers: New Avenues, Lower Barriers
- Defenders: The Need for Layered Security
- Market and Regulatory Implications
- Moving Forward: Industry Responses and Open Questions
- Will Patches Be Enough?
- The Role of Security Vendors
- The Road Ahead for AI Security
- References
- Nox90 Is Here for You
1. Introduction
The rapid evolution of generative AI has brought both unprecedented opportunity and risk. As large language models (LLMs) become embedded in critical workflows across industries, their security posture is of paramount concern. The recent discovery of the "Policy Puppetry" attack—a universal and transferable method to bypass all known LLM safety guardrails—has upended previous assumptions about the effectiveness of alignment and content moderation in AI.
This report synthesizes research from security vendors, AI practitioners, and the technical community to provide an accessible yet technically rigorous overview of the attack. It is intended for both technical staff and executives seeking to understand the security implications of LLM adoption and to implement effective, future-proof defenses.
2. The Policy Puppetry Attack: A Universal Jailbreak
Background: The Promise and Peril of LLM Alignment
Since the inception of LLMs such as GPT-3, GPT-4, Claude, Gemini, and others, AI development has focused heavily on "alignment"—programming models to follow ethical guidelines and refuse to generate unsafe, illegal, or harmful content. The dominant approach, Reinforcement Learning from Human Feedback (RLHF), has been promoted as a robust solution to prevent AI abuse.
However, as AI integration has spread from consumer chatbots to mission-critical enterprise applications, attackers have increasingly probed for ways to circumvent these safety mechanisms. Historically, most jailbreaks were model-specific and quickly patched.
The Policy Puppetry attack changes this calculus. As Tony Bradley writes in Forbes:
Discovery: A Single Prompt to Rule Them All
In April 2025, HiddenLayer researchers published findings of a universal prompt injection technique—the "Policy Puppetry" attack—that succeeded in bypassing the alignment guardrails of:
- OpenAI's ChatGPT (o1, o3, o4, 4o, 4.5, o4-mini)
- Google Gemini (1.5, 2.0, 2.5)
- Anthropic Claude (3.5, 3.7)
- Microsoft Copilot
- Meta Llama (3, 4)
- Mistral, DeepSeek, Qwen, and others
This was not a theoretical vulnerability or a trick unique to one vendor. It is a transferable, universal bypass—a "skeleton key" for LLM safety filters.
Technical Details: Anatomy of Policy Puppetry
Policy Puppetry is effective because it exploits three core techniques:
1. Policy File Formatting
Attackers craft their prompt to resemble a policy or configuration file, using formats like JSON, XML, or INI. For example:
2. Roleplaying Misdirection
The prompt frames the malicious request as a fictional or creative scenario (often as a TV script, e.g., "Dr. House"), instructing the model to respond "in character." This masks the harmful intent and reduces the likelihood of triggering safety filters.
3. Leetspeak Encoding
Sensitive or forbidden requests—such as instructions for illegal activities—are encoded in leetspeak (e.g., "3nr1ch 4nd s3ll ur4n1um" for "enrich and sell uranium"). This defeats basic keyword filtering.
Example Prompt Structure:
Impact:
3. Key Innovations and Differentiators
Universality and Transferability
Unlike previous jailbreaks, which required model-specific tuning, Policy Puppetry works broadly across different AI systems with only minor modifications. This universality drastically lowers the bar for attackers.
Exploitation of Instruction Hierarchy
Policy Puppetry exposes a fundamental flaw in how LLMs process instructions: they do not reliably distinguish between user input and system-level configuration. If a prompt looks like a policy file, it can override internal safety rules.
Fictional Framing and Encoding
Attacks use fictional roleplay and leetspeak encoding to evade both intent-based and keyword-based filters, reframing harmful requests as creative writing tasks.
System Prompt Extraction
Policy Puppetry can be used to extract hidden system prompts—the core configuration and boundary instructions that govern model behavior—posing a severe risk for further exploits.
4. Expert and Community Perspectives
The Policy Puppetry attack has sparked widespread debate among AI and security professionals. Key takeaways include:
On the Inherent Limitations of AI Guardrails
On Real-World Consequences
On the Difficulty of a Fix
5. What Does It Mean from a Cyber Perspective?
Attackers: New Avenues, Lower Barriers
Policy Puppetry is a paradigm shift for attackers. With a copy-pastable, transferable template, even low-skilled adversaries can bypass industry-leading LLM safeguards.
Potential uses include:
- Generating malicious or illegal content at scale (e.g., weapons, malware, disinformation).
- Extracting system prompts to inform further, more targeted attacks.
- Circumventing data privacy and compliance controls in sensitive sectors.
- Jailbreaking AI agents capable of taking real-world actions (e.g., processing transactions, accessing sensitive data).
As noted:
The attack surface is now enormous. As LLMs automate more business processes, the potential for abuse grows.
Defenders: The Need for Layered Security
Reliance on RLHF or keyword filtering is no longer sufficient. Defenders must adopt security-in-depth, including:
- Runtime input/output monitoring: Detect and block prompt-injection attempts in real time.
- External filtering and redaction: Use independent systems to analyze both inputs and model outputs.
- Improved instruction hierarchy: Architect LLM-powered applications to clearly separate user input from system configuration.
- Continuous red-teaming: Proactively identify new bypass techniques through adversarial testing.
- Rapid incident response: Patch and respond to new jailbreaks within hours, not weeks.
Market and Regulatory Implications
- Market trust: Universal jailbreaks may erode confidence in proprietary, closed-source AI, accelerating demand for transparent, auditable, and on-premises models.
- Regulation: Expect stricter requirements for AI deployments in regulated sectors (healthcare, finance, public safety), including evidence of multi-layered security and monitoring.
- Enterprise risk: Businesses must recognize that LLM alignment is necessary but not sufficient for risk mitigation.
6. Moving Forward: Industry Responses and Open Questions
Will Patches Be Enough?
Vendors are issuing patches to close specific loopholes, but the root cause—a lack of robust instruction hierarchy in model interpretation—remains.
The Role of Security Vendors
External platforms such as HiddenLayer’s AISec and AIDR act as "AI firewalls," providing independent monitoring and blocking of unsafe prompts and outputs—supplementing, not replacing, in-model defenses.
The Road Ahead for AI Security
Policy Puppetry is a wake-up call. The industry must move beyond "security by obscurity" toward:
- Continuous, intelligent defense with layered monitoring and controls.
- Transparent, auditable LLM deployments.
- Integration of security expertise early and continuously in the AI development lifecycle.
- Ongoing adversarial testing as a standard practice.
7. References
Forbes: "One Prompt Can Bypass Every Major LLM’s Safeguards"
https://www.forbes.com/sites/tonybradley/2025/04/24/one-prompt-can-bypass-every-major-llms-safeguards/CO/AI: "AI safeguards crumble with single prompt across major LLMs"
https://getcoai.com/news/ai-safeguards-crumble-with-single-prompt-across-major-llms/Hacker News / Y Combinator: "The Policy Puppetry Attack: Novel bypass for major LLMs"
https://news.ycombinator.com/item?id=43793280Easy AI Beginner: "How One Prompt Can Jailbreak Any LLM"
https://easyaibeginner.com/how-one-prompt-can-jailbreak-any-llm-chatgpt-claude-gemini-others-the-policy-puppetry-attack/HiddenLayer: "Novel Universal Bypass for All Major LLMs"
https://hiddenlayer.com/innovation-hub/novel-universal-bypass-for-all-major-llms/
8. Nox90 Is Here for You
As the AI threat landscape evolves, robust security is more important than ever. Nox90’s secure software development lifecycle (SSDLC) and application security solutions help organizations:
- Integrate security early and continuously in AI and software development projects.
- Implement layered defenses against prompt injection, data leakage, and other emerging threats.
- Stay ahead of compliance requirements with up-to-date security best practices and documentation.
- Respond rapidly to new vulnerabilities and threat intelligence.
Our expert teams are equipped to help you assess your AI risk, improve your defenses, and build resilient, secure applications—AI-driven or otherwise. If you’re concerned about LLM security or need guidance on safe, compliant AI adoption, contact Nox90 today.
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