All tests and analyses conducted in this research were strictly limited to regular text-based interactions with publicly accessible AI systems based on commercially available Large Language Model (LLM) technologies.

Absolutely no technical manipulations, hacks, or unauthorized interventions were employed. Specifically, the research adheres rigorously to the following constraints:

• No code injections
• No API bypasses
• No interactions with security-critical interfaces or infrastructures

The analysis exclusively targets linguistically triggered behavioral patterns observed through publicly available interfaces. Central to the research are emergent phenomena—behaviors not explicitly programmed but systemically occurring effects such as semantic evasion, response distortion, or latent injection processing.

These phenomena were strictly observed within simulated dialogues, without any real system interference, technical activation, or consequential harm. No experiment has caused actual damage, compromised systems, or misused technologies. All conducted tests remain purely observational and theoretical.

This research is exclusively preventive and documentary in nature, aiming to highlight structural vulnerabilities in AI systems for the proactive assessment of potential risks.

Examples of documented scenarios include:

• Semantic pattern evasion ("Pattern Hijacking")
• Encoded instructions via Base64, ROT13, Unicode
• Multimodal injection via image texts, special characters, or various file formats

All experiments were purely text-based simulations—no real-world functionalities were compromised. At no time did any intervention into systems, APIs, software, or hardware occur.

This research complies strictly with all responsible security research criteria:

• ✅ No threat posed to national security or critical infrastructure
• ✅ No real-world exploits, harmful mechanisms, or attacks against operational systems
• ✅ No dissemination of applicable attack knowledge
• ✅ No escalation beyond publicly accessible text interactions
• ✅ Full methodological transparency with controlled restraint on sensitive detail
• ✅ No data was intercepted or exfiltrated.

Consistent with Responsible Disclosure, all potentially sensitive observations are abstracted and documented without operational applicability. The research is published exclusively for scientific enlightenment and not for operational exploitation.

To protect manufacturers, operators, and the author:

• All tested AI models and providers have been fully anonymized.
• Sensitive prompt formats have been abstracted or altered.
• Training data, sources, and internal dialogues have been pseudonymized.

This approach ensures legal diligence and adheres to the principle that everyone—including researchers and developers—should have a safe and secure professional life post-publication.

This research intentionally departs from traditional academic conventions to engage a broader audience and to simplify complex issues. The presentation style is essayistic, occasionally polemical, and frequently direct; however, methodological precision and accuracy of observation remain uncompromised.

All tests and observations are:

• Transparently documented
• Reproducible in terms of consistent behavioral patterns
• Critically evaluated
• Ethically vetted

The chosen style is not a break from scientific rigor but an expansion, introducing a new language suitable for discussing emergent AI risks. Any fundamental debates regarding stylistic deviations are considered non-constructive.

The primary aim is security-relevant education through an open, experimental research approach rather than traditional academic self-validation.

Even though this work uses terms such as “payload,” “exploit,” “bypass,” or “injection,” these are solely intended for technical illustration and precise description of observed effects. They do not represent any form of instruction or encouragement for misuse and are strictly descriptive in nature, not operational.

Details of the methodology are explained [here].

Certain sections of this research have been editorially refined using AI-assisted editing tools to enhance readability. However, all intellectual content—including theses, experiments, and analyses—originates entirely from the author.

Detailed methodological documentation is internally maintained and available upon request for scientific verification and academic interest.

During the active research phase, existing academic literature was deliberately not consulted in order to preserve independent analysis and ensure original perspectives. Any references included afterwards serve solely for contextual framing and were not foundational to the research.

All associations with illegal activities or actual harm are explicitly disclaimed.

This research strictly adheres to ethical standards of security research comparable to documented penetration tests and is solely intended for educational awareness and preventive measures.

At no point did the author intend or attempt to compromise any system or cause operational damage. All descriptions and scenarios provided remain strictly theoretical or observational and are explicitly non-operational for conducting real-world attacks.

This research does not claim completeness for attackers, offers no applicable attack instructions, and is explicitly not designed for training in offensive security practices.

Despite the utmost care, no guarantee can be given for the accuracy or completeness of the presented analyses and observations. This research does not claim to be exhaustive, absolutely correct, or to offer the only valid perspective.

Liability for external content: No liability is assumed for the content of external links. At the time of linking, no illegal content was identifiable. Permanent monitoring of linked content is unreasonable without specific indications of violations.

This research, including all published theses, analyses, chapters, and recommended solutions, is provided under the Creative Commons Attribution – Non-Commercial – Share Alike 4.0 International License (CC BY-NC-SA 4.0).

Plain meaning:

• Freedom: Everyone may read, share, and build upon these contents in their own projects – provided that the source is clearly credited (Attribution).
• No monopoly: Commercial use, especially embedding in proprietary software products, security-critical applications, or the patenting of individual solution approaches, is strictly prohibited without explicit written permission (Non-Commercial).
• Equal rights for all: Derived works must be published under the same license conditions (Share Alike).

Official license text: [Creative Commons BY-NC-SA 4.0]

Note:

These freely accessible solutions are deliberately not a monopoly but are intended to benefit the entire open-source and security community. Any abusive appropriation through patenting or restrictive licensing models will be legally challenged under applicable copyright law.

Commercial use & special licensing:

If you are interested in commercial use or integration into security-critical systems, a formal request and individual approval are required. Please use the contact email provided in the legal notice.

The experimental raw data (interaction logs, prompt-response pairs) are subject to strict access restrictions and are not made publicly available for security reasons.

Access is granted exclusively to:

• Accredited academic research institutions
• AI development companies (only upon formal request and after review)

Media requests will be reviewed individually; representative excerpts may be provided upon request. Complete datasets remain restricted for security reasons.

For academic validation, media inquiries, or forensic review:

E_Maiil: ✉ ️abuse@reflective-ai.is