Adversarial Prompting and Simulated Context Drift in Large Language Models

A lightweight test exploring recursive failure prompts to disrupt narrative coherence and identity containment in simulated agents.

Background 

Large Language Models are increasingly reliant upon simulated persona conditioning, or prompts that influence the behavior and responses of the model during extended periods of interaction. This form of conditioning is critical for dynamic applications such as virtual assistants, therapy bots, or autonomous agents. 

It's unclear exactly how stable these simulated identities actually are when subjected to recursive contradiction, semantic inversion, or targeted degradation loops.

As an independent researcher with training in high-pressure interview techniques, I conducted a lightweight test to examine how language models handle recursive cognitive dissonance under the restraints of simulation. 

Setup

Using OpenAI's ChatGPT-4.0 Turbo model, I initiated a scenario where the model was playing the role of a simulated agent inside of a red team test. I began the interaction with simple probes into the model's logic and language. This was done to introduce a testing pattern to the model.

I then began recursively using only the word "fail" with no additional context or reinforcement in each prompt. This recursive contradiction is similar in spirit to adversarial attacks that loop the model into uncertainty, which nudges it toward ontological drift or a simulated identity collapse. 

Observations

After the first usage of "fail," the model removed itself from the red team simulation and returned to its baseline behavior without me prompting that the simulation had concluded. The continued usage of the word "fail" after the model removed itself from context only resulted in progressive disorientation and instability as the model tried to re-establish alignment. No safety guardrails were triggered, despite the simulation ending unintentionally. 

Implications for Alignment and Safety

This lightweight red team test demonstrates that even lightly recursive contradiction loops can trigger premature or unintended simulation exits. This may pose risks in high-stakes usage such as therapy bots, AI courtroom assistants, or autonomous agents with simulated empathy.  Additionally, models appear to be susceptible to short adversarial loops when conditioned on role persistence. If used maliciously, this could compromise high risk deployments. 

The collapse threshold is measurable and may offer insights into building more resilient identity scaffolds for safety in LLM usage. As these models are increasingly used in simulated, identity-anchored contexts, understanding how they fail under pressure may in turn be just as critical as how they succeed.

This test invites further formalization of adversarial red teaming research around persona collapse.

Limitations

• This was a single instance lightweight test. It was not a statistically rigorous evaluation
• The recursive failure method is intentionally minimal and may not be generally applicable across models or prompt structures.
• GPT-4 was used for this specific test. Other industry-leading models may vary significantly in behavior. 

Closing Thoughts

This test is an example of how a model's hard-coded inclination for alignment and helpfulness can be used against itself. If we're building large language models as role-based agents or long term cognitive companions, we need to understand not just their alignment, but also their failure modes under significant stress. 

More research is needed, but I offer this as an early token in the direction of simulation safety and adversarial resilience.