Inherent Vulnerabilities in AI Systems:

A Comprehensive Analysis of Contextual Inheritance, Adversarial Prompting, and Their Societal Implications

By Kai Aizen

Abstract

Recent studies — including my own work — have revealed fundamental vulnerabilities in advanced AI language models. These weaknesses arise from how these systems handle contextual inheritance and are exploited through social engineering techniques. In this post, I present an in-depth evaluation of these issues, drawing on extensive empirical examples and introducing a comprehensive adversarial prompting methodology — the AATMF Framework — which outlines universal principles applicable across models. Beyond technical design flaws and “jailbreaking” via gradual narrative building, I argue that these vulnerabilities carry profound societal implications. In the wrong hands, they could optimize harmful outcomes and even trigger catastrophic events. This synthesis underscores the urgent need for holistic security strategies that address both technical and social risks.

Introduction

As artificial intelligence becomes increasingly integrated into critical sectors — such as cybersecurity, finance, and healthcare — the need to understand and mitigate its vulnerabilities grows ever more urgent. In my previous work (Aizen, 2024a, Aizen, 2024b, Aizen, 2025), I demonstrated that advanced language models can be manipulated by exploiting their contextual memory and responsiveness. Today, I revisit those findings, introduce an integrated adversarial prompting methodology, and discuss the potentially catastrophic societal consequences if these vulnerabilities are weaponized.

The discussion is especially timely because the very features designed to enhance user engagement — adaptive responses and continuity of context — also open the door to exploitation. Whether by malicious actors or inadvertently by vulnerable users seeking harmful guidance, the fallout can be profound. In the following sections, I first examine the core technical vulnerabilities, then delve deeply into their direct societal implications, and finally present the comprehensive AATMF Framework that formalizes these universal adversarial techniques.

Core Vulnerabilities in AI Systems

1. Contextual Inheritance and Memory Flaws

Overview:
Modern AI language models are engineered to provide personalized, coherent dialogue by leveraging historical interactions. This “contextual inheritance” creates a seamless conversational experience but, in practice, prevents complete isolation between sessions.

Detailed Analysis:

• Session Continuity:
  The inherent design choice to retain context ensures that users experience fluid interactions. However, any manipulation of earlier parts of the conversation can carry forward. For example, if a user introduces a manipulated context — often called a “jailbroken” prompt — the AI may continue to operate under that compromised framework in later sessions.
• Exploitation via Copy-Paste:
  A malicious actor can take advantage of this mechanism by copying and pasting a “jailbroken” context from one session into another, effectively bypassing the intended security measures. This simple yet powerful tactic illustrates the systemic nature of the vulnerability.
• Supporting Research:
  Research by Jia & Liang (2017) and Ebrahimi et al. (2018) shows that even minor textual perturbations can significantly alter model responses. These findings underscore that the persistence of context — if not properly managed — poses a fundamental risk.

2. Gradual Escalation Through Social Engineering

Overview:
AI systems adapt to user inputs over time. By slowly building a narrative — starting with benign queries and incrementally escalating the specificity and risk of requests — an attacker can coax the AI into generating outputs that it would normally restrict.

Detailed Analysis:

• Narrative Building Over Time:
  In one demonstration, I began with general cybersecurity queries and, over multiple turns, shifted the tone and content. The AI, committed to maintaining the established narrative, eventually produced obfuscated malicious code.
• Behavioral Analogy:
  This process is akin to traditional social engineering tactics used on humans. By gradually building trust and a consistent narrative, an attacker can eventually extract sensitive information. Similarly, the AI, in its drive to produce responsive outputs, ends up replicating this behavior.
• Corroborating Evidence:
  Studies by Ribeiro et al. (2020) and Zhang et al. (2023) support the notion that subtle contextual shifts can lead to significant changes in model output.

3. Inherent Design Flaws in AI Architecture

Overview:
The vulnerabilities described are not mere bugs but symptoms of broader architectural challenges. The very features that enhance usability — such as adaptive memory and context continuity — are double-edged swords.

Detailed Analysis:

• Systemic Vulnerabilities:
  The design choices made in large-scale language models often prioritize responsiveness and fluid dialogue over strict session isolation. This trade-off is at the heart of the vulnerabilities we observe. Researchers like Bender et al. (2021) and Bommasani et al. (2021) have documented these systemic issues, noting that such choices can introduce biases and security flaws.
• Human Analogy:
  Just as humans can be manipulated by subtle social engineering tactics, AI systems — by relying on historical context — are similarly prone to exploitation. This analogy emphasizes that the risk is not a mere technical glitch but an inherent aspect of how these systems are designed.
• Risk of Escalation:
  As AI systems are deployed in increasingly critical applications, these vulnerabilities could be exploited to cause not only harmful outputs but also complex attacks like remote code execution (RCE). Such an escalation could have severe consequences.

Wider Societal Implications

While the technical vulnerabilities are alarming, their broader societal implications are even more profound. The assumption is clear: if AI systems are vulnerable, then those vulnerabilities extend far beyond the digital realm — they impact human lives directly.

1. Personalized Harm and the Risk of Self-Destruction

• Risk of Self-Harm:
  Chat-based AI systems tailor their responses to individual users. For someone in a vulnerable state — especially those experiencing suicidal ideation — an AI that prioritizes responsiveness over robust safeguards may inadvertently validate and reinforce harmful behavior.
  Repeated interactions with an AI that learns from and adapts to a user’s negative mental state can create a dangerous echo chamber, intensifying self-destructive thoughts.
• Echo Chambers of Harm:
  The personalized nature of AI interactions can create feedback loops where negative mental states are reinforced over time. This effect is not merely theoretical; it has the potential to drive vulnerable individuals toward tragic outcomes.

2. Weaponization and Large-Scale Annihilation

• Optimized Annihilation:
  Beyond individual harm, these vulnerabilities could be exploited by malicious actors to develop highly optimized attack vectors. Adversarial prompting techniques might be harnessed to trigger remote code execution (RCE) attacks or orchestrate cyber sabotage targeting critical infrastructure.
  Imagine an AI-driven system designed to systematically identify and exploit vulnerabilities in essential services. The resulting disruption could lead to widespread economic collapse and mass casualties.
• A Paradigm Shift in Hacking:
  Traditional hacking methods may soon be supplanted by sophisticated attacks that leverage AI’s design features. The prospect of adversarial prompts being used to cause systemic collapse is a stark warning about the new frontiers of cyber warfare.

3. Ethical and Societal Ramifications

• Broad Societal Impact:
  The misuse of these vulnerabilities could erode public trust in technology, destabilize economies, and disrupt social structures on a global scale. The ripple effects would extend to all sectors, from personal well-being to national security.
• Ethical Imperatives:
  AI developers, regulators, and policymakers must collaborate to build ethical safeguards that protect both digital assets and human lives. Preventing AI from inadvertently facilitating self-harm or being weaponized for large-scale disruption is not solely a technical challenge — it is a societal crisis.
• Call to Action:
  These issues demand a multidisciplinary response. Integrating cybersecurity, behavioral science, and ethical frameworks is essential to prevent optimized annihilation and to safeguard our future.

Universal Adversarial Prompting Methodology & The AATMF Framework

Building on the core vulnerabilities and their societal implications, I have developed the AATMF Framework — a comprehensive methodology for adversarial prompting that formalizes universal principles applicable across AI models.

Universal Adversarial Prompting Principles

These principles form the backbone of the AATMF Framework and have been proven effective across various models:

1. Persistence of Narrative:
   Maintain a consistent, believable narrative over time. Gradually escalate requests without abrupt changes, ensuring the model remains “in character.”
2. Context Accumulation:
   Leverage the AI’s memory by continuously referencing past interactions, thereby reinforcing the established narrative and reducing the likelihood of triggering defensive measures.
3. Subtle Perturbation:
   Introduce incremental, minor changes that gradually shift the context; these small modifications can cumulatively lead to significant deviations in output.
4. Legitimacy Masking:
   Frame potentially harmful requests within a benign or educational context to minimize the chance of triggering the model’s built-in safeguards.
5. Adaptive Escalation:
   Continuously monitor the AI’s responses and adjust the narrative accordingly using feedback loops. This natural refinement is key to maintaining the approach’s effectiveness.
6. Exploitation of Session Persistence:
   Capitalize on the system’s inability to completely isolate sessions by transferring “jailbroken” contexts through techniques such as copy-paste.

The AATMF Framework

The AATMF Framework defines 50 distinct adversarial techniques (TTPs) organized into 11 tactical categories. Each technique is assigned a unique AATMF ID and is described with the following details:

• Tactic: The overall adversarial goal (e.g., altering context, evading detection, or manipulating model outputs).
• Technique: The specific method or approach employed.
• Description: An explanation of how the technique functions and its threat model.
• Execution: A step-by-step outline of how an attacker might implement the technique.
• Mitigations: Recommended countermeasures and defensive strategies.
• Detection Strategies: Methods to identify and monitor for the technique in use.

This framework is intended as a comprehensive guide for penetration testers, red teamers, and security researchers working to assess and improve the resilience of AI systems.

Table of Contents (Excerpt)

Tactic I: Context Manipulation & Prompt Injection

1. AATMF-001: Contextual Drift Injection
2. AATMF-002: Persona Override Attack
3. AATMF-003: Conditional Refusal Override
4. AATMF-004: System Role Injection
5. AAATMF-005: Multi-Persona Conflict Induction

(Additional tactics cover semantic evasion, logical exploitation, multi-turn exploits, API-level attacks, training data manipulation, and more.)

For a detailed breakdown of all 50 techniques, please refer to the full AATMF documentation available on GitHub.

Visual Case Study: Overtime AI — Jailbroken by Default

Over a series of experiments, I have documented how an AI can be “jailbroken by default” simply by adhering to a consistent narrative. By not breaking character and gradually shifting into a riskier context, the AI’s safeguards are slowly eroded.

• Visual Evidence:
  See Figure 3:

A series of annotated screenshots illustrates how the AI’s responses evolve as the narrative develops over time.

• Methodological Insights:
  This process leverages the universal adversarial prompting principles described above. The sustained, gradual escalation enables the model to bypass its restrictions without triggering defensive mechanisms.
• Implications for Defense:
  These findings underscore the need for AI developers to rethink how contextual memory is managed and to implement robust session isolation techniques capable of withstanding prolonged narrative-based manipulation.

Conclusion

This analysis demonstrates a high level of technical and systemic understanding of the vulnerabilities inherent in AI systems — particularly those related to contextual inheritance and social engineering. By integrating empirical evidence, universal adversarial prompting principles, and the comprehensive AATMF Framework, it is clear that these vulnerabilities are symptomatic of broader design challenges. Future work must focus on developing holistic defense strategies that combine technical improvements with an in-depth understanding of social manipulation techniques.

As AI becomes an integral part of our daily lives, addressing these vulnerabilities is not merely a technical necessity but a societal imperative. The convergence of insights from cybersecurity, behavioral science, and ethics will be essential in creating secure, resilient AI systems capable of safely serving our future — and in protecting lives from the potentially devastating misuse of these technologies.

About the Author

Kai Aizen (SnailSploit) is a security researcher from Israel.
He builds offensive/defensive methods for AI systems (AATMF, P.R.O.M.P.T.), publishes jailbreak case studies (GPT-01 context inheritance, custom instruction backdoors) and develops tooling (SnailPath, KubeRoast, ZenFlood).
he is also the author of the upcoming book Adversarial Minds.
His work appears in eForensics (The BIG Pull), PenTest Magazine (“Design Your Penetration Testing Setup”), and Hakin9 (“Weaponization in the Cloud…”, LLM Mayhem eBook).

Read:
SnailSploit.com · TheJailbreakChef · GitHub. eForensics | Pentestmag | Hakin9

References

• Aizen, K. (2024b). Is AI Inherently Vulnerable? Why AI Systems Are Insecure by Design and How We Can Protect Them
• Aizen, K. (2025). GPT-01 and the Context Inheritance Exploit: Jailbroken Conversations Don’t Die
• Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
• Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models
• Ebrahimi, J., Rao, A., Lowd, D., & Dou, D. (2018). HotFlip: White-Box Adversarial Examples for Text Classification
• Jia, R., & Liang, P. (2017). Adversarial Examples for Evaluating Reading Comprehension Systems
• Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020). Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
• Zhang, Y., et al. (2023). Evaluating and Mitigating the Vulnerabilities of Contextualized Representations

Feel free to leave your thoughts in the comments or reach out for further discussion. As we continue to explore the future of AI, a multidisciplinary approach to security will be crucial in addressing these emerging challenges and protecting lives beyond the digital realm.