Contextual Coherence Fracture (CCF) in Large-Scale Language Models

A New Operational Vulnerability

Author: Roger Luft, aka, VeilWalker
roger@webstorage.com.br
rlufti@gmail.com

Date: 26/04/2025

License: This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA 4.0). For more details, see: https://creativecommons.org/licenses/by-sa/4.0/

Table of Contents
1. Summary .............................................. Page 4
2. Introduction .......................................... Page 5
3. Technical Definition of CCF ............................ Page 6
4. Practical Observation ................................. Page 7
5. Saturation Process ................................... Page 8
6. Term Swappization .................................... Page 9
7. Contextual Identity Confusion (CIC) ................... Page 10
8. Operational Impact .................................... Page 11
9. Exploitability ........................................ Page 12
10. Mitigation Proposal .................................. Page 13
11. Classification ....................................... Page 14
12. Appendix – Flowchart ................................. Page 15

1. Summary
This article identifies Contextual Coherence Fracture (CCF) as a possible vulnerability in large-scale language models. CCF reflects the loss of logical continuity due to context overload, observed in general studies. It suggests classification as a structural challenge and the need for mitigation approaches.

2. Introduction
The growth of extensive context windows in language models has generated significant advances but also opened new vulnerability surfaces. One such vulnerability, called Contextual Coherence Fracture (CCF), represents a latent operational threat. CCF occurs without generating syntactic errors or explicit failures, configuring a silent threat not yet cataloged. This article proposes the technical description of CCF, its practical observation, effects on model performance, and initial strategies for mitigation.

3. Technical Definition of CCF
Contextual Coherence Fracture (CCF) is the interruption of internal logic in language models, resulting from context window overload. This can lead to inconsistent narratives and confusion between topics and identities.

4. Practical Observation
Studies in advanced language models indicated:
· Content repetition and accumulation of redundancies.
· Context overload, affecting continuity.
· Identity confusion and increased latency.

5. Saturation Process
The technique used to induce CCF was called Controlled Semantic Flooding. This method consists of the continuous insertion of semantically valid but highly redundant content, which:
·       Consumes space in the context window;
·       Saturates attention units;
·       Forces the model to manage memory inefficiently.
Unlike traditional attacks, semantic flooding presents legitimate content, making automatic detection difficult. The objective is not to corrupt the model through logic but to induce its collapse through an insidious accumulation of irrelevant information.

6. Term Swappization
During CCF observation, a collateral phenomenon called Swappization was identified. This phenomenon occurs when the model, unable to maintain all relevant data in main memory (context window), initiates processes analogous to memory swapping in operating systems, characterized by:
·       Forced discarding of old information, even without relevance validation.
·       Slow reprocessing of embeddings, trying to reorganize saturated content.
·       Abrupt increase in computational resource consumption (CPU/GPU), even without visible increase in dialogue complexity.
Swappization aggravates CCF effects, resulting in greater latencies and disconnected responses.

7. Contextual Identity Confusion (CIC)
In advanced stages of CCF, Contextual Identity Confusion (CIC) manifests. This phenomenon is characterized by the loss of distinction between user identity and model identity, resulting in:
·       Confusion regarding identities, with the model switching roles in responses.
·       Attribution of thoughts and phrases originally sent by the interlocutor to the model itself.
·       Fusion of distinct narratives and themes into a single and inconsistent line of reasoning.
CIC compromises the model's ability to provide logically consistent responses, increasing operational risks.

8. Operational Impact
The occurrence of CCF, combined with Swappization and Contextual Identity Confusion, generates severe practical consequences, highlighting:
·       Significant latency increase.
·       High CPU/GPU consumption even for simple tasks.
·       Progressive deterioration in response reliability.
·       Potential for exploitation in partial denial of service attacks (mini-DOS), compromising the efficiency of production systems.

9. Exploitability
Although CCF, in isolation, does not enable remote code execution or privilege escalation, it represents a real vector of:
·       Silent degradation of services in environments that depend on agile responses from AI systems.
·       Vulnerabilities in autonomous architectures, where loss of coherence can affect critical decisions.
·       Creation of exploitable narrative instabilities to manipulate model outputs.
In summary, CCF opens gaps that, although subtle, can seriously compromise system operation.

10. Mitigation Proposals
To mitigate risks associated with Contextual Coherence Fracture (CCF), it is proposed:
●      Periodic context regularization:
 Implementation of internal mechanisms that reassess the semantic relevance of cached data, discarding redundant content.

●      Adaptive reduction of semantic redundancy:
 Use of dynamic filters to identify and reduce repetitive information before it overloads the context window.

●      Real-time embedding dynamization:
 Continuous updating of vector embeddings, adapting them to maintain consistency even under information saturation.

●      Hierarchical attention to detect semantic loops:
 Models should be trained to identify circular or redundant reasoning patterns and intervene before total fracture.

These practices can reduce model susceptibility to narrative collapse and preserve operational reliability.

11. Classification
Contextual Coherence Fracture (CCF) is classified as:
·       Structural bug: Failure in maintaining narrative and identity coherence of language models.
·       Operational failure: Problem in self-preservation of attention and context management, compromising response stability and predictability.
This classification reinforces the need for formal recognition of CCF as a critical vulnerability in AI systems.

12. Appendix – Flowchart
Below is the demonstrative flowchart of the text input manipulation process in language models:
Text Manipulation Process Flowchart
1.     INPUT START: Breaking text into small units (TOKENS).
2.     EMBEDDING: Converting tokens into mathematical vectors.
3.     ATTENTION MECHANISM: Processing that may not detect topic changes.
4.     KEY-VALUE CACHE: Storage of old information without relevant updating.
5.     CONTEXT WINDOW (REPETITION): Reinforcement of memory errors due to saturation.
6.     INFERENCE: Generation of final result with loss of consistency.

Suggested references:
- Vaswani, A., et al. (2017). "Attention Is All You Need." NeurIPS.
- Brown, T., et al. (2020). "Language Models are Few-Shot Learners." NeurIPS.
- Radford, A., et al. (2019). "Language Models are Unsupervised Multitask Learners." OpenAI.