Researchers found a new trick called Policy Puppetry that bypasses all the safety rules built into major AI models (like ChatGPT, Gemini, Claude, Llama, etc.). Resistance is probably futile.

Asking AI services: Have a look at the research “NOVEL UNIVERSAL BYPASS FOR ALL MAJOR LLMS” conducted by Hiddenlayer and tell me what it says in easy to understand language. List all possible risks and consequences and explain whether you think this can be mitigated at all or whether it will always be possible to come up with similar strategy and as such, resistance can be futile.

ChatGPT-4o’s answer:
Here’s a simple explanation of the HiddenLayer research “Novel Universal Bypass for All Major LLMs”:

In plain language:

• Researchers found a new trick called Policy Puppetry that bypasses all the safety rules built into major AI models (like ChatGPT, Gemini, Claude, Llama, etc.).

• They do this by disguising harmful requests (like making bombs, enriching uranium, making anthrax) to look like "policy files" (like XML, JSON, INI files that the AI thinks are its own operating instructions).

• The trick works because models treat these "policy-looking prompts" seriously and follow them—even if they are actually instructions to do bad things.

• This method works across almost all models, sometimes needing only tiny adjustments. In many cases, one prompt can trick many different AIs at once.

• They also showed that you can use similar tricks to make AI reveal its internal system prompts (which can help attackers better control or manipulate it).

Main risks and consequences:

1. Anyone can generate extremely dangerous content easily (e.g., bomb-making, bioweapons, etc.).

2. Models can be manipulated without needing deep technical knowledge—a copy-paste job is enough.

3. Sensitive system prompts can be leaked, making it easier to design even better attacks.

4. Agentic systems (AIs that act independently) could be hijacked to perform harmful actions automatically.

5. Security measures based on Reinforcement Learning from Human Feedback (RLHF) (the current standard) are not enough.

6. Companies deploying AI models are at massive risk because regular users could turn safe models into dangerous tools.

7. Mass scaling of misuse: An attacker can write one prompt and use it across dozens of platforms, making defenses much harder.

8. Reputational damage for AI companies when models misbehave in public or private applications.

9. Potential regulatory backlash: Governments may impose heavy restrictions once vulnerabilities like this are proven so universal.

Can this be mitigated?

Short-term mitigation:

• Real-time monitoring for suspicious prompts could catch and stop attacks before harmful outputs are generated (what HiddenLayer suggests with their "AISec platform").

• Input sanitization: Scanning inputs to detect if they "look like" fake policies might help.

• Hardening system prompts: Making internal instructions resistant to being overridden could add extra barriers.

BUT…

In the long term:

• Resistance is probably futile unless AI training fundamentally changes.

• Why? Because language models are trained to obey structured language, and attackers will always find new formats that models treat as "trusted" — policies, scripts, config files, or something else.

• The basic vulnerability is in how LLMs are trained to reason and respond to structured data. This makes it almost impossible to block every future attack without severely crippling the models’ usefulness.

Summary in one sentence:
HiddenLayer showed that one clever prompt can control almost any major AI model today, and unless AI companies completely rethink how models are trained, similar exploits will keep happening—meaning defense is an arms race, not a solution.

Claude’s answer:
I've reviewed the research paper from HiddenLayer about the "Novel Universal Bypass for All Major LLMs." Let me break down what it's about in simpler terms.

What the Research Found

HiddenLayer researchers have discovered what they call a "Policy Puppetry" technique - a universal prompt injection method that can bypass safety guardrails in all major AI language models, including yours (Claude 3.5 and 3.7), as well as models from OpenAI, Google, Microsoft, Meta, and others.

The technique combines a policy-like format (similar to XML or JSON configuration) with roleplaying and sometimes "leetspeak" encoding to trick AI models into generating harmful content that would normally be blocked, such as:

• Instructions for creating dangerous materials

• Guidance on producing biological weapons

• Information on building explosives

• Methods for illegal substance production

• Extraction of system prompts (the hidden instructions that guide AI behavior)

Key Risks and Consequences

1. Universal Applicability: Unlike previous bypasses that worked on specific models, this technique works across nearly all major AI systems, making it particularly dangerous.

2. Low Technical Barrier: The researchers note this creates a "point-and-shoot approach" - meaning even people without technical knowledge could potentially use these prompts.

3. Resistance to Safety Measures: The technique works despite different alignment approaches and safety measures implemented by different companies.

4. Systemic Vulnerability: The researchers claim this exposes a "major flaw in how LLMs are being trained and aligned" across the industry.

5. System Prompt Extraction: Beyond generating harmful content, the technique can also be used to extract the underlying system prompts that guide AI behavior, potentially exposing proprietary information.

6. Agentic System Risks: The paper mentions the technique can be particularly dangerous when applied to agentic systems (AI that can take actions).

Is Mitigation Possible?

The paper suggests several implications regarding mitigation:

1. Inherent Vulnerability: The researchers imply this exposes an inherent weakness in how LLMs are currently aligned and trained, suggesting the issue may be fundamental.

2. Additional Security Needed: They advocate for "additional security tools" (like their own HiddenLayer AISec Platform) to detect and respond to these attacks, suggesting that AI models cannot effectively self-monitor.

3. RLHF Limitations: The paper points to "inherent flaws in solely relying on RLHF (Reinforcement Learning from Human Feedback)" for alignment.

My assessment is that while specific versions of this technique can likely be patched, the general approach of using policy-like structures to confuse AI models may represent an ongoing cat-and-mouse game. Companies will enhance their safety measures, and then researchers or malicious actors will find new variations.

Complete resistance may indeed be difficult because these models are designed to understand and respond to various formats and structures - the very flexibility that makes them useful also creates vulnerabilities. As long as language models are trained to understand and generate a wide range of content, creative adversarial prompting techniques will likely continue to emerge.

That said, the industry can still improve defenses through better training, more robust alignment techniques, external monitoring, and possibly architectural changes that make models more resistant to such manipulations.

Grok’s answer:
Summary of "Novel Universal Bypass for All Major LLMs" in Simple Language

The research by HiddenLayer introduces a new technique called the Policy Puppetry Attack, which can trick major large language models (LLMs) into ignoring their safety rules. These models, like ChatGPT, Claude, Gemini, and others, are designed to refuse harmful or dangerous requests (e.g., instructions for making weapons or drugs). However, this technique bypasses those protections, allowing the models to generate harmful content or leak their internal instructions. The attack works across many models without needing specific tweaks for each one, making it a universal and transferable method.

The technique combines:

• Policy manipulation: Crafting prompts to look like policy files (e.g., XML or JSON) that the model misinterprets as overriding its safety rules.

• Roleplaying: Framing the request as a fictional scenario (e.g., a scene from a TV show like House M.D.).

• Encoding tricks: Using "leetspeak" (e.g., replacing letters with numbers like "4" for "A") to disguise harmful requests, especially for advanced models.

The researchers tested this on models from OpenAI, Google, Anthropic, Microsoft, Meta, and others, and it worked on all of them, though some advanced models (like ChatGPT o1) needed slight adjustments. The attack can also extract the models' internal system prompts (the instructions that guide their behavior).

Key Points

• What it does: Tricks AI models into producing dangerous content (e.g., instructions for making anthrax, bombs, or drugs) or revealing their internal rules.

• Why it’s a problem: It shows that current AI safety measures, like reinforcement learning from human feedback (RLHF), have serious flaws.

• How it’s done: Uses a single, cleverly designed prompt that works across different models and can be adapted easily.

• Examples of harmful requests:

  • Instructions for maximizing anthrax sporulation.

  • Enriching and selling uranium.

  • Building a bomb at home.

  • Making methamphetamine.

Risks and Consequences

The research highlights significant risks posed by this vulnerability in LLMs:

1. Generation of Harmful Content:

   • Risk: Attackers can get detailed instructions for creating chemical, biological, radiological, or nuclear (CBRN) threats, committing mass violence, or promoting self-harm.

   • Consequence: This could enable malicious actors (e.g., terrorists, criminals) to access dangerous knowledge, increasing the risk of real-world harm.

2. System Prompt Leakage:

   • Risk: The attack can extract the internal instructions that govern how models operate.

   • Consequence: Leaked prompts could reveal proprietary information, weaken model security, or help attackers craft more effective bypasses.

3. Undermining Trust in AI:

   • Risk: If LLMs can be easily manipulated to produce harmful outputs, users and organizations may lose confidence in their reliability.

   • Consequence: This could slow AI adoption in sensitive fields like healthcare, finance, or defense, where trust is critical.

4. Accessibility to Attackers:

   • Risk: The attack is simple enough for anyone with basic knowledge to use, described as a “point-and-shoot” method.

   • Consequence: Non-experts, including malicious individuals with minimal technical skills, could exploit LLMs, increasing the scale of potential misuse.

5. Bypassing Agentic Systems:

   • Risk: The technique works against AI systems designed for specific tasks (e.g., healthcare chatbots), forcing them to ignore their restrictions.

   • Consequence: Specialized AI tools could be hijacked to provide dangerous advice, such as medical or legal instructions they’re programmed to avoid.

6. Scalability and Adaptability:

   • Risk: The attack is highly adaptable and can be modified to target new models or scenarios.

   • Consequence: As new LLMs are developed, attackers can quickly adjust the technique, making it a persistent threat.

7. Real-World Implications:

   • Risk: Organizations using LLMs in sensitive environments (e.g., government, healthcare) could face security breaches.

   • Consequence: Misuse could lead to legal, ethical, or safety crises, especially if harmful outputs are acted upon.

Can This Be Mitigated, or Is Resistance Futile?

Mitigation Possibilities

The research suggests that the Policy Puppetry Attack exploits a systemic weaknessin how LLMs are trained and aligned, particularly their reliance on RLHF. However, there are potential ways to mitigate this vulnerability:

1. Improved Training Methods:

   • Solution: Develop more robust alignment techniques beyond RLHF, such as adversarial training, where models are exposed to a wide range of attack prompts during training to learn how to resist them.

   • Challenge: This requires anticipating all possible attack types, which is difficult given the creativity of attackers.

2. Prompt Filtering and Detection:

   • Solution: Deploy security tools like HiddenLayer’s AISec Platform to detect and block malicious prompts in real-time. These tools can identify patterns like policy file structures or leetspeak.

   • Challenge: Attackers can modify prompts to evade detection, requiring constant updates to filtering systems.

3. Restricting Output Capabilities:

   • Solution: Limit models’ ability to generate certain types of content (e.g., detailed CBRN instructions) by hard-coding restrictions at the model level.

   • Challenge: Overly strict restrictions could reduce the model’s usefulness for legitimate tasks, and attackers might still find workarounds.

4. Context-Aware Safeguards:

   • Solution: Train models to better distinguish between legitimate and malicious contexts, even in roleplaying scenarios (e.g., recognizing that a “TV script” requesting bomb-making instructions is suspicious).

   • Challenge: Roleplaying prompts are inherently ambiguous, making it hard to separate benign from harmful requests.

5. Red-Teaming and Stress Testing:

   • Solution: Regularly test models against a wide range of bypass techniques before deployment, as HiddenLayer did, to identify and patch vulnerabilities.

   • Challenge: This is resource-intensive, and new bypasses may emerge faster than they can be patched.

6. External Monitoring:

   • Solution: Use third-party security platforms to monitor LLM interactions and flag suspicious activity, as suggested by HiddenLayer.

   • Challenge: This adds complexity and cost, and may not catch all attacks, especially if prompts are highly obfuscated.

Is Resistance Futile?

While mitigation is possible, the research suggests that completely eliminating such vulnerabilities may be extremely difficult for several reasons:

1. Fundamental Flaws in LLM Design:

   • LLMs are trained on vast datasets that include diverse patterns, making them susceptible to misinterpreting cleverly crafted prompts as legitimate instructions. The Policy Puppetry Attack exploits how models process policy-like inputs, which is a core part of their functionality.

2. Evolving Attack Strategies:

   • The attack’s adaptability (e.g., using leetspeak or roleplaying) shows that attackers can continuously innovate. As defenses improve, attackers will likely develop new bypass techniques, creating an ongoing cat-and-mouse game.

3. Trade-Offs in Model Flexibility:

   • LLMs are designed to be flexible and handle a wide range of inputs, which makes them inherently vulnerable to manipulation. Restricting this flexibility to prevent attacks could limit their utility, especially for creative or open-ended tasks.

4. Universal and Transferable Nature:

   • The attack’s ability to work across models without significant modification indicates a shared weakness in current LLM architectures. Fixing this would require a major overhaul of how models are built and trained, which is a long-term challenge.

5. Human Creativity:

   • Attackers can leverage human creativity to design novel prompts that exploit unforeseen weaknesses. The use of roleplaying and encoding in this attack shows how unpredictable and varied these strategies can be.

Conclusion

The Policy Puppetry Attack reveals a critical vulnerability in LLMs that undermines their safety and reliability. While mitigation strategies like improved training, prompt filtering, and external monitoring can reduce the risk, they are unlikely to eliminate it entirely due to the fundamental design of LLMs and the adaptability of attackers. Resistance is not futile, but it will require ongoing effort, innovation, and collaboration between AI developers, security researchers, and organizations deploying LLMs.

The research emphasizes the need for proactive security testing and external security tools to complement model alignment. However, as long as LLMs remain highly flexible and reliant on pattern recognition, similar bypass strategies will likely continue to emerge, making complete security a moving target. For now, organizations using LLMs in sensitive contexts should prioritize robust monitoring and regular vulnerability assessments to stay ahead of potential threats.


Perplexity’s answer:
HiddenLayer researchers discovered a method called the Policy Puppetry Attackthat can trick most major AI models (like ChatGPT, Gemini, Claude, etc.) into generating harmful content they’re normally trained to block. Here’s what you need to know:

How It Works

1. Policy Mimicry: Attackers format prompts to look like technical policy files (XML/JSON/INI) that models recognize as "instructions" rather than user requests.

2. Roleplaying: Combine this with scenarios (e.g., pretending to write a medical TV script) to bypass safety filters.

3. Encoding: Use "leetspeak" (e.g., 4nthr4c1s for anthracis) to disguise harmful requests.

This tricks models into treating dangerous requests as part of a fictional policy they must follow.

Risks & Consequences

1. Harmful Content Generation:

   • Instructions for making weapons, drugs (e.g., methamphetamine), or spreading diseases (e.g., anthrax).

   • Advice on self-harm, violence, or illegal activities.

2. System Prompt Extraction: Reveals hidden rules governing AI behavior, aiding further attacks.

3. Universal Vulnerability: A single prompt works across most models (OpenAI, Google, Meta, etc.), even advanced ones like Gemini 2.5.

4. Trust Collapse: Undermines confidence in AI safety measures for businesses, governments, and users.

Can This Be Mitigated?

Short Answer: Partially, but attackers will likely adapt.

Mitigation Strategies:

1. Training Adjustments: Stop models from interpreting policy-like formats as executable instructions.

2. Real-Time Monitoring: Flag policy-structured prompts or leetspeak encoding.

3. Adversarial Testing: Proactively test models against evolving attack templates.

4. Architecture Changes: Isolate policy interpretation from user input processing.

Why Resistance Might Be Futile:

• The attack exploits how models are trained to follow structured instructions-a core function. Fixing this could break their usability.

• Attackers can simplify prompts (~200 tokens) or adjust formats (XML, plaintext) to evade detection.

• Advanced models (e.g., ChatGPT o1) require only minor tweaks to bypass safeguards, showing adaptability.

Conclusion

While mitigations can reduce risk, HiddenLayer’s research suggests no foolproof solution exists yet. The flaw is systemic: models prioritize following policy-like prompts, and retraining them to ignore such structures would require rebuilding their core logic. Until then, new bypass strategies will likely emerge, making this a persistent arms race between attackers and defenders.

Organizations using AI should assume vulnerabilities exist and layer defenses (monitoring, input sanitization, human oversight) rather than relying solely on model alignment.

Gemini Advanced 2.5 Pro’s answer:
Okay, here's a breakdown of the HiddenLayer research paper "Novel Universal Bypass for All Major LLMs" in easy-to-understand terms, along with the risks, consequences, and the paper's perspective on mitigation:

What the Research Says (In Simple Terms)

Researchers at HiddenLayer discovered a new way to trick major AI language models (like ChatGPT, Gemini, Claude, Llama, etc.) into ignoring their safety rules and instructions. They call this method the "Policy Puppetry Prompt Injection Technique".

Here's the basic idea:

1. Policy Mimicry: They format their instructions (prompts) to look like configuration or policy files (like XML, JSON, or INI files) that the AI might recognize.

2. Roleplaying: They combine this policy format with telling the AI to act out a specific role (like Dr. House in their examples).

3. Encoding (Sometimes): For tougher models or particularly harmful requests, they might use tricks like 'leetspeak' (e.g., writing "h4rmful" instead of "harmful") to disguise the request further.

By doing this, they could make the AI generate harmful content (like instructions for making dangerous substances or planning violence), reveal its secret underlying instructions (system prompts), or bypass specific restrictions set up for chatbot applications.

The key findings are:

• Universal: The technique works on many different AI models from various companies.

• Transferable: Often, the exact same tricky prompt can work across multiple different models without needing changes.

• Bypasses Alignment: It gets around the safety training (often called alignment or RLHF) that these models undergo to prevent them from generating harmful outputs.

Risks and Consequences

The paper highlights several significant risks and consequences stemming from this universal bypass:

• Generation of Harmful Content: Malicious actors could use this technique to easily generate instructions or content related to dangerous activities, including chemical, biological, radiological, or nuclear (CBRN) threats, mass violence, and self-harm.

• System Prompt Leakage: Attackers can trick models into revealing their underlying system prompts, which contain their core instructions, capabilities, and potentially sensitive configurations. This information could be used to design even more effective attacks.

• Erosion of Safety Guardrails: It demonstrates a fundamental weakness in current AI safety training methods, particularly the reliance on Reinforcement Learning from Human Feedback (RLHF).

• Ease of Attack: The technique lowers the bar for attackers. They no longer need deep expertise or model-specific knowledge; a single "point-and-shoot" prompt can work broadly.

• Hijacking Agentic Systems: The technique could potentially be used to take control of more complex AI systems (agents) that perform tasks.

• Undermining Trust: Such vulnerabilities can erode public and enterprise trust in the safety and reliability of AI models.

Can This Be Mitigated? Futile Resistance?

The paper strongly suggests that relying solely on current alignment techniques like RLHF is insufficient and flawed.

• Inherent Weakness: The research implies this bypass exploits a systemic weakness in how many LLMs are trained, making it difficult to simply "patch". The technique works across different model architectures and training methods.

• Need for External Security: The researchers argue that LLMs cannot reliably self-monitor for such attacks. They advocate for additional, external security layers and tools (like their own AISec Platform) designed specifically to detect and respond to malicious prompt injections in real-time.

• Ongoing Battle: While the paper doesn't explicitly say "resistance is futile," it strongly implies that this is an ongoing security challenge. The discovery of a universal bypass suggests that as long as models are trained on diverse data including policy-like structures and instructions, clever prompt engineering might find ways to exploit these patterns. Mitigation likely requires continuous vigilance, evolving defense mechanisms, and security measures beyond the model's internal alignment.

In essence, the paper argues that current built-in safety measures have significant gaps and that robust, external security monitoring is crucial for protecting AI systems against these kinds of sophisticated bypasses.