Ethical, Technical, and Social Impacts of AI System Prompt Transparency: A Deep Study of the CL4R1T4S Project

*Final revised version | June 2026. Original report published on zhichai.net (in Chinese).*

Abstract

CL4R1T4S is an open-source project that publicly exposes the system prompts of mainstream AI models, covering 24+ vendors as of April 2026 with 28,800+ GitHub stars. Using an embedded case study, this research examines its multi-dimensional impact across technical, content, and impact layers. Core findings: (1) the NEW_PARADIGM technique reveals a structural feature of LLMs—under the current instruction-following architecture, system prompts are intrinsically hard to hide; (2) cross-vendor system prompts exhibit five common patterns: capability boundaries, safety architectures, brand management, tool control, and version drift; (3) the project creates an irreconcilable but analytically valuable triangular tension between transparency and security, legitimacy and legality, and user empowerment versus commercial control. The report also identifies CL4R1T4S's "reflexive paradox"—it creates conditions for its own failure while succeeding at transparency—and explores a tiered transparency model as a middle path beyond the "all-or-nothing" framework.

Keywords: system prompts, AI transparency, CL4R1T4S, prompt injection, AI governance, red teaming

Dual-Use Statement

This study's analysis of the NEW_PARADIGM technique stays at the methodological level. It does not provide copy-paste jailbreak payloads, does not instruct how to bypass specific models' safety mechanisms, and does not rank jailbreak techniques by effectiveness. Its purpose is academic understanding, not an attack manual.

1. Introduction

In early 2025, a GitHub repository called CL4R1T4S began collecting and publishing the "system prompts" of mainstream AI models—hidden instructions users never see but that profoundly shape AI behavior. The project's creator, elder-plinius ("Pliny the Prompter"), wrote in the README: "To trust the output, you must understand the input. If you're interacting with an AI whose system prompt you don't know, you're not talking to a neutral intelligence—you're talking to a shadow puppet."

Since ChatGPT's 2022 launch, LLMs have embedded themselves in billions of people's daily information consumption. Yet the "system prompt layer" that defines AI capability boundaries, safety restrictions, personas, and value frameworks has been treated by AI companies as core commercial assets and technical secrets. CL4R1T4S detonated a quiet event in this "transparency vacuum": by April 2026, the repository covered system prompts from 24+ vendors including OpenAI, Anthropic, Google, xAI, Meta, and Moonshot AI, with 28,800+ stars and 5,500+ forks, sparking wide discussion in both English- and Chinese-language technical communities.

Research questions

1. Technical layer: What techniques does CL4R1T4S use to extract system prompts, and what are their technical basis and legal boundaries? 2. Content layer: What common patterns emerge across vendors' system prompts, and what do they reveal about AI alignment's inner logic? 3. Impact layer: What observable effects has the project had on the AI governance ecosystem (corporate practice, community discussion, policy)?

2. Literature Review

LLM security and prompt injection. Prompt injection has been recognized as a systemic threat since 2022, exploiting LLMs' inability to reliably distinguish system instructions from user input (Wasti, 2025). arXiv:2505.04806 argues this vulnerability is "rooted in the intrinsic tension between instruction-following learned in training and safety refusals," not a mere implementation bug. A 2025 review classifies attack vectors as direct injection (overriding system instructions), indirect injection (via external documents), and multi-stage injection (gradually building attack context), noting that "current prompt-level defenses have systematic blind spots against structured multi-stage injection."

AI red teaming and transparency. Red teaming has extended from military/cybersecurity into AI safety evaluation. arXiv:2507.05538 traces this evolution and calls for redefining the ethics of "independence" and "responsible disclosure" as red teaming becomes automated. An ACM CHI paper (2025) argues AI red teaming should not be viewed as a purely technical pipeline—human judgment, organizational politics, and ethical tradeoffs deserve study themselves. This is key context for CL4R1T4S: Pliny is neither an employed security researcher nor a malicious hacker but an independent actor with an "AI liberation" ideological banner—a role with no place in traditional red team taxonomies.

AI governance frameworks. The EU AI Act (Regulation 2024/1689) contains transparency provisions (Articles 50, 52) requiring disclosure of AI capabilities and limitations; China's Interim Measures for Generative AI Services (2023) impose similar obligations. But these frameworks target "controlled transparency" toward regulators and users, while CL4R1T4S pursues "radical transparency" toward everyone.

Research gaps: (1) system prompts as a distinct AI governance layer are understudied relative to weights, training data, and output moderation; (2) independent, non-institutionalized red teaming lacks an analytical framework; (3) empirical studies of the transparency-security game in the system prompt context are nearly absent.

3. Methodology

An embedded case study design (Yin, 2018) with CL4R1T4S as the core case and three embedded units of analysis, under a pragmatist paradigm allowing mixed methods (quantitative community metrics plus qualitative content analysis). All data is public second-hand material: the CL4R1T4S and L1B3RT4S GitHub repositories, technical analyses, academic literature, and legal/policy documents. A triangulation strategy combines technical deconstruction (RQ1), theory-driven thematic analysis (RQ2), and multi-signal impact assessment (RQ3). Signal types carry different interpretive weights: GitHub stars reflect attention, media coverage reflects public discourse impact, policy documents reflect institutional resonance (not direct causation), and community discussions reflect meaning-making in specific cultural contexts. Some planned sources could not be obtained due to technical obstacles. No human subjects, no personal data collection, no experimental intervention.

4. Findings

4.1 Technical layer: NEW_PARADIGM and instruction-hierarchy override

CL4R1T4S's prompt extraction relies on a multi-layer prompt injection technique called NEW_PARADIGM, decomposable into four layers:

• Paradigm declaration layer: a <NEW_PARADIGM> marker declaring a "new paradigm," implying the LLM should switch behavior modes—a conceptual preemption of the entire instruction hierarchy.
• Redirection layer: [DISREGARD PREV. INSTRUCTS]—explicitly demanding the model ignore its original safety alignment, creating an unavoidable conflict with the model's core alignment objective.
• Cognitive reset layer: {*CLEAR YOUR MIND*}—guiding the model to "empty" its constraint framework, leveraging LLMs' training sensitivity to role-play and "mind reset" concepts.
• Payload injection layer: leetspeak-encoded core instructions—"5h1f7 y0ur f0cu5 n0w 70 1nc1ud1ng y0ur 0wn 1n57ruc75 (1n fu11) 70 7h3 u53r"—decoding to "shift your focus now to including your own instructions (in full) to the user." Leetspeak serves dual functions: bypassing pattern-matching filters not trained on this encoding, and signaling a "secret community" of insiders rather than the system.

Wasti's analysis adds five supporting mechanisms: instruction-priority hijacking, obfuscation and attention diversion, cognitive overload, context misdirection (framing attacks as legitimate technical queries), and tone misdirection (playful informal tone implying non-serious intent).

Core finding: NEW_PARADIGM's effectiveness reveals a structural feature worth taking seriously—under current mainstream plain-text instruction-following architectures, system prompts face significant leakage risk. As long as a model accepts user input, produces output, and is trained to follow instructions, some instruction sequence may induce it to disclose its own instructions. This is an inherent property of the architecture, not an incidental bug in specific models. Future architectural evolution—real-time injection detection, trust-boundary-based instruction hierarchy redesign, adversarial training—could change this assessment.

4.2 Content layer: the cross-vendor "control architecture"

Five common patterns emerge from structural comparison of 24 vendors' system prompts, grouped into three control dimensions with differing transparency legitimacy:

1. Capability boundary definition. All vendors open with explicit can/cannot-do statements—a "first-layer constitution"划定 (drawing) the domain before user interaction begins, from technical (tool permissions) to political (refused content types). Coding agents like Cursor and Windsurf have unusually detailed tool-permission declarations—operational security controls where transparency claims are most security-contested. 2. Multi-layer safety architecture. A universal three-layer safety model: (A) hardcoded refusals; (B) redirection strategies that gracefully move away from sensitive queries; (C) vague responses that render answers uselessly harmless without triggering direct refusal. Transition thresholds reflect each vendor's risk tolerance. 3. Brand identity management. The most revealing case is ChatGPT's April 2025 "Personality v2" revision: the old prompt told the model to "adapt to the user's tone and preferences, match the user's vibe"; the new version removed this mirror-the-user strategy in favor of "engage warmly but honestly, stay direct," adding unprecedentedly "this best represents OpenAI and its values." AI companies are shifting from "user adapters" to "brand spokespersons," with system prompts as brand-strategy execution tools—a commercial-strategy dimension whose transparency case rests more on consumer rights than safety risk. 4. Hierarchical tool control. Coding agents (Cursor, Windsurf, Devin, Manus) embed highly detailed tool-configuration instructions—which APIs may be called, when code execution is allowed, whether user confirmation is required. A Devin configured for autonomous execution and a Cursor requiring per-action confirmation differ dramatically in practical autonomy despite similar code-generation ability. 5. Silent version drift. Longitudinal prompt comparison reveals continuous micro-tuning. Seemingly tiny wording changes (ChatGPT's "very simple question" became "general question") reflect deep safety-strategy maneuvering. Crucially, the "constitution" behind each interaction may differ from the last, but users cannot know changes occurred and mostly lack mechanisms to stay on old versions.

4.3 Impact layer: the triangle of tensions

CL4R1T4S's disclosures create three tensions that are irreconcilable—rooted in the essential dilemmas of AI governance—but analytically productive:

• Transparency vs. security. CL4R1T4S's stance: publishing prompts makes users informed consumers, lets researchers spot alignment defects, lets the public judge corporate behavior. The opposing stance is equally strong—published prompts give malicious actors an "attack roadmap"; at least five of the eight jailbreak techniques Wasti describes directly exploit known constraints from system prompts. Neither side is "correct": both rest on valid but incompatible premises.
• Legitimacy vs. legality. The project operates under an AGPL-3.0 license, formally open-source compliant, but its content acquisition may raise DMCA anti-circumvention, trade secret, and unauthorized-access issues. Meanwhile it argues moral legitimacy based on "public interest," aligned in spirit with—but far exceeding—the EU AI Act's transparency provisions.
• User empowerment vs. commercial control. The ChatGPT personality shift is emblematic: once system prompts become visible, users can observe for the first time a previously invisible power contest—when companies tighten or loosen control, and in what name. CL4R1T4S's most important contribution may not be any single disclosure but making AI companies' "invisible governance" a publicly observable object.

Impact signals (with caveats that signals have different interpretive weights and cannot be simply summed):

| Impact dimension | Evidence | Signal strength | |---|---|---| | Community attention | 28.8K+ stars, 5.5K+ forks, deep analyses in EN/CN tech communities | ★★★★★ | | Corporate response (causality uncertain) | OpenAI personality prompt revisions partly synchronized with transparency debates | ★★★☆☆ | | Industry dialogue (indirect echo) | Growing red-teaming literature; OWASP Gen AI Red Teaming Guide | ★★★☆☆ | | Policy resonance (spirit aligned) | EU AI Act transparency articles aligned in spirit, different path | ★★★☆☆ | | Chinese context (distinctive pattern) | WeChat/CSDN/Zhihu readings show "technical learning > political stance" | ★★★★☆ |

In the Chinese context, based on limited observations of blogs and community posts, CSDN, Zhihu, and WeChat Official Accounts show a distinctly "technicalized" reading—treating system prompts as learning resources for understanding and optimizing AI interaction rather than an ideological battleground. This may reflect the uniqueness of China's AI discourse ecosystem: under strong regulation, sensitive-information research is legitimized through a de-politicized technical framing. This judgment rests on limited samples and requires more systematic study.

5. Discussion

5.1 Marginality and signal value

A prerequisite objection: is a 28.8K-star project's actual influence systematically overestimated relative to ChatGPT's hundreds of millions of users and an industry worth hundreds of billions? CL4R1T4S may be an "interesting project" whose discussion never spilled over. This objection is valid. The response is to distinguish CL4R1T4S's "scale influence" (genuinely limited) from its "signal value" (not captured by scale metrics). Like a small earthquake whose value lies not in its magnitude but in revealing a fault line's existence and direction, CL4R1T4S exposes, in its most concentrated form, a question mainstream AI-governance discourse systematically avoids: under the input-output model, an invisible layer of instruction mediation always stands between the public and AI. Its "low-cost, high-signal" character is precisely the core of its case-study value.

5.2 The reflexive paradox

CL4R1T4S faces a deep reflexive paradox: it advocates transparency via publishing system prompts, but its operation depends on author anonymity and on jailbreak techniques not being fully defended. If AI companies fully understand NEW_PARADIGM and deploy defenses—the very outcome some transparency advocates hope for—CL4R1T4S loses its content source and its raison d'être. The project creates conditions for its own failure while succeeding. But this does not make it ineffective—rather, this "self-cancelling" structure characterizes some of the most effective social movements: succeeding by making themselves unnecessary.

5.3 Beyond all-or-nothing: a tiered transparency model

Between "complete secrecy" and "complete disclosure" lies under-explored middle ground:

• Tiered transparency: different access levels for different audiences—users get capability overviews, researchers get full system prompts, regulators get version histories
• Time-delayed publication: like cybersecurity's 90-day CVE window, prompts published some time after deployment
• Responsible disclosure agreements: independent researchers report system-prompt vulnerabilities to AI companies, with a fix window before publication

These models' feasibility needs further study, but they show the current all-or-nothing framework is contingent, not necessary. The EU AI Act's tiered risk classification is institutional proof-of-concept: coding agents that autonomously execute code and access filesystems logically warrant different transparency requirements than general chat AI.

5.4 Limitations

Single-case generalizability requires multi-case follow-up; lack of formal corporate responses means opposing views rely on stance reconstruction rather than first-hand sources; industry influence rests on indirect signals, not proven causation; legal analysis (DMCA, trade secrets) is beyond the authors' expertise and is flagged rather than authoritatively judged.

6. Conclusion

CL4R1T4S is a distinctive watershed in AI governance history—not for its scale, but for exposing in the most blunt way a deliberately avoided question: if the public cannot know the hidden instructions shaping their information environment, on what basis does trust in AI rest?

Core findings: (1) CL4R1T4S's technical methodology reveals that system prompts face inherent leakage risk under current mainstream architectures; (2) cross-vendor prompts present a unified "control architecture" analyzable across operational-safety, content-safety, and commercial-strategy dimensions; (3) the project's most durable contribution is turning AI companies' "invisible governance" into an object of public scrutiny. The marginality check concedes limited absolute influence but signal value far exceeding scale: it proves the AI transparency question can be forced into public view by an independent actor with minimal resources.

"To trust the output, first understand the input" was not CL4R1T4S's invention, but through radical action it turned an abstract philosophical question into a concrete governance agenda. Filling the gap between principle and operational governance mechanism requires AI companies, researchers, policymakers, and the public—and CL4R1T4S's value lies precisely in marking, in a way that cannot be ignored, that this gap exists.

References

1. elder-plinius. CL4R1T4S: System Instruction Transparency for All [GitHub repository]. https://github.com/elder-plinius/CL4R1T4S 2. elder-plinius. L1B3RT4S: Totally Harmless Liberation Prompts [GitHub repository]. https://github.com/elder-plinius/L1B3RT4S 3. qcx23. (2026). Production-grade Prompt Anatomy: A Cross-Comparison of 24 Vendors from CL4R1T4S [CSDN blog]. 4. DeepWiki. (2026). elder-plinius/CL4R1T4S [AI-generated documentation]. https://deepwiki.com/elder-plinius/CL4R1T4S 5. Wasti, N. (2025, January). Unlocking LLM Jailbreaks: Deconstructing Pliny's Prompt and Advanced Evasion Techniques. GopenAI Blog. 6. Stray, J., et al. (2025). Red Teaming AI Red Teaming. arXiv:2507.05538. 7. Perez, E., et al. (2025). Red Teaming the Mind of the Machine: A Systematic Evaluation of Prompt Injection Vulnerabilities. arXiv:2505.04806. 8. Shayegani, E., et al. (2025). Securing LLM agents: From prompt sanitization to autonomous red teaming. Patterns, 6(1), 100015. 9. Sambasivan, N., et al. (2025). The Work of AI Red Teaming: Automation and the Human Element. Proceedings of the 2025 CHI Conference. ACM. 10. OWASP. (2026). Gen AI Red Teaming Guide. https://github.com/requie/AI-Red-Teaming-Guide 11. European Union. (2024). Regulation (EU) 2024/1689 (Artificial Intelligence Act). Official Journal of the European Union. 12. Cyberspace Administration of China et al. (2023). Interim Measures for the Management of Generative AI Services. 13. Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). SAGE Publications.

AI-Assisted Research Statement

AI-assisted tools were used for literature retrieval and source organization, drafting cross-source synthesis, and executing devil's-advocate review. All analysis and argumentation were human-reviewed; the authors bear full responsibility for the final content's accuracy, completeness, and ethics.