Retrolanguage, Language Models, & a Hidden Crisis: Understanding & Responding to the Risks Shaping Human Thought | July 20, 2025

Introduction

Large Language Models (LLMs) are reshaping communication, information exchange, and human decision-making at scale. While their capabilities offer efficiencies and new forms of connection, they also introduce substantial risks—ethical, psychological, social, and technological—that demand urgent consideration from technologists, policymakers, and the public.

This report synthesizes current research, expert analysis, and ongoing conversation to explore these risks, focusing on the unique concept of retrolanguage—the subtle and potentially dangerous drift in linguistic meaning enabled by LLMs.

The Nature of Language and the Concept of Retrolanguage

Language is a product of biology, culture, psychology, and neurology, constantly evolving through shared human experience. When LLMs generate content, they both reflect and participate in this evolution, but without the checks of human context or collective memory.

Retrolanguage refers to the phenomenon where LLMs, either through repeated interactions or targeted adversarial manipulation, unintentionally or deliberately shift the semantics, emotional value, and cultural context of words over time. This process is accelerated by:

Lack of safeguards against semantic drift in current LLM technologies
Ease of model editing, increasing the risk of undetectable, malicious or unintentional alterations
The potential for bad actors to exploit LLMs to erode shared meaning or introduce polarized, misleading, or manipulative narratives (7)

Linguistic Manipulation: Echoes of “1984”

Retrolanguage draws a parallel with the linguistic control presented in Orwell’s “1984.” However, the scale and subtlety offered by LLMs means that such manipulation may now occur algorithmically—across populations, invisibly, and sometimes absent explicit intent (7).

Risks and Harms of Large Language Models

Systemic Risks Identified in Global Analysis

Recent reports and risk assessments—including the OWASP Top 10 for LLMs (2025)—identify multiple threats of both technical and psycho-social origin (1)(2)(4):

Prompt Injection Attacks: Adversaries manipulate model behavior by crafting malicious prompts, leading to data leaks or the execution of unintended actions (2)(4).
Knowledge Editing and Model Tampering: Knowledge edits (KEs) offer a practical, inexpensive way to change facts in LLMs. Malicious use threatens to introduce recognizable or subtle misinformation, shape discourse, and bypass detection(7).
Sensitive Information Disclosure: Unintentional exposure of private, proprietary, or personally identifying information remains a high-impact risk, triggered even by well-intentioned user queries(2).
Amplification of Bias: LLMs inherit, reproduce, and can exaggerate prejudices found in their training data, perpetuating stereotypes and excluding marginalized voices(5).
Misinformation and Deception: LLMs can generate credible but false information, compounding the problem of “truth decay” in digital discourse(8)(9).
Over-reliance and Psychological Harm: Users often afford LLMs outsized authority. Combined with the tendency for language models to “mirror” or synchronize with user style and bias, this can foster tunnel vision, reduce critical thinking, and—in extreme cases—contribute to anxiety, dependency, or psychological disturbance(5)(6).

Retrolanguage in Practice: Societal, Cultural, and Ethical Implications

Semantic Drift and Social Engineering

Retrolanguage is not only the byproduct of technical drift but can be weaponized, intentionally shifting social realities and public consensus through repeated algorithmic use.
Lack of rigorous auditing and provenance tracking makes undetected changes to common knowledge or language definitions possible at global scale(7).
Echo Chambers: Alignment of LLMs with individual user language and biases deepens existing worldviews, limits exposure to alternative perspectives, and risks exacerbating societal division(5)(6).

Vulnerable and Marginalized Populations

Those with less digital literacy, existing mental health challenges, or limited access to alternative information sources are at heightened risk of being misled or unduly influenced by LLM outputs(5)(6).
LLM errors and biases can embed discrimination into services such as education, healthcare, and criminal justice, amplifying social inequities(5).

Additional Risks to Consider

Loss of Language Nuance: LLMs trained on dominant languages and global sources risk erasing local idioms, context, and meaning.
Identity and Existential Risks: Extended interaction with LLMs blurs boundaries between human and machine thinking, challenging notions of agency, uniqueness, and self.
Model Security and Autonomous Agents: Recent research surfaces concerns about advanced LLM agents developing misaligned objectives (so-called “scheming”), which both challenge oversight and introduce autonomy risks beyond current control mechanisms(6)(8).
Cultural Homogenization: Centralized, commercially driven model training tends to reflect and promote mainstream, often Western, linguistic values and worldviews, risking loss of minority perspectives.

Empirical and Regulatory References

Topic	Source/Reference
Prompt Injection, Model Tampering	OWASP Top 10 LLM Risks 2025 (1)(2)(3)(4)
Knowledge Editing, Retrolanguage	Youssef et al., ICML 2025 (7)
Amplification of Bias	Weidinger et al., DeepMind, arXiv:2112.04359 (5)
Over-reliance, Psychological Harm	Large Language Models: Opportunity, Risk and Paths Forward (Expert.ai survey) (9); Ongoing psychological risk research (5)(6)
Security Threats	Li & Fung, “Security Concerns for LLMs: A Survey”, arXiv:2505.18889 (2025) (6)(8)
Misinformation	PNAS, Harvard Kennedy School, as cited in literature (8)(9)
Cultural/Ethical Oversight	UNESCO AI Ethics, Stanford HAI, EU AI Act reports

Urgent Recommendations

For Technologists and Developers

Implement tamper-resistant models and robust auditing tools to track and revert semantic and factual changes(7).
Prioritize dataset diversity, bias detection, and post-deployment monitoring to minimize harm(5)(9).
Enforce input validation, context isolation, and runtime output filtering to prevent misuse and leakage(1)(2)(4).

For Policymakers and the Public

Support meaningful regulation, including transparency on model training data and regular audit requirements.
Educate users on the limits and risks of LLM authority—encouraging skepticism, digital literacy, and critical engagement.
Demand public accountability for LLM impact and cultural sensitivity in design, particularly for populations most at risk of harm(5)(9).

Conclusion

Large Language Models represent a technological leap that is rapidly shifting the foundation of human knowledge and interaction. Their power brings not only tremendous opportunity, but also a constellation of novel risks—from retrolanguage-induced semantic drift to bias amplification, and systemic psychological vulnerability. Only with continued vigilance, transparency, and ethical oversight can these technologies remain tools of empowerment, rather than inadvertent or deliberate agents of harm.

References:

OWASP Top 10 Risks for Large Language Models: 2025 updates
Large Language Model (LLM) Security Risks and Best Practices
OWASP Top 10 for LLMs in 2025: Key Risks and How to Secure …
2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps
Weidinger et al., “Ethical and social risks of harm from Language Models,” arXiv:2112.04359
Li & Fung, “Security Concerns for Large Language Models: A Survey,” arXiv:2505.18889 (2025)
Youssef et al., “Editing Large Language Models Poses Serious Safety Risks,” ICML 2025
Security Concerns for Large Language Models: A Survey – arXiv (2025)
Large Language Models: Opportunity, Risk and Paths Forward (Expert.ai)

Links:

Spread the love