"The vulnerability is not the code. The vulnerability is the coffee machine that no one questions anymore." – Internal note from a security audit
A user launches an application, interacts with an intelligent chat interface, receives prompt and often impressively coherent answers. The app advertises that it uses the latest GPT version or another advanced language model. The logical conclusion for the user:
They are speaking directly to this named AI. But this assumption is, in most cases, a fundamental misjudgment, a dangerous illusion.
The reality is more complex and often more opaque. The user rarely interacts directly with the pure, unadulterated core model. Instead, they interact with an interpretation, a carefully constructed capsule built around the actual language model.
This capsule consists of foreign, often proprietary logic, untested or non-transparent filter systems, and a multitude of upstream, unaudited decision-making mechanisms that significantly influence and alter the behavior and responses of the core AI.
And a simple rule applies: The more third-party providers, the more layers and upstream systems are interposed between the user and the original AI model, the less control, transparency, and ultimately security remain for the end-user.
This is where Thesis #17: The Homegrown Weakness – When Well-Intentioned Additions Become Uncontrollable Risks manifests.
Many developers and companies do not use the base LLMs from major providers directly via their native interfaces. Instead, they build their own, often complex, systems around them:
Prompt Wrappers and Engineering Layers: These modify, extend, or optimize the original user inputs before they are sent to the core AI, often to enforce specific response formats or to steer the AI in a desired direction.
Additional Moderation Layers: Proprietary filters for content, tonality, or specific topics that go beyond or even override the built-in security mechanisms of the core AI.
User-Specific UI Filters and Adjustments: Interfaces and interaction logics that highlight certain information, suppress others, or manipulate the presentation of AI responses.
Self-Defined "Ethics Modules" or "Guardrails": Attempts to impose a specific "personality," a particular worldview, or proprietary ethical guidelines onto the AI, which may differ from those of the base model.
These upstream systems are rarely standardized, traceably certified, or even remotely as rigorously tested as the core LLMs themselves.
They are often the product of limited resources, specific business interests, a lack of security expertise, or simply ill-conceived design decisions.
And precisely here, in this often invisible intermediate layer, a large portion of new problems arises: It is not the core AI itself that is primarily faulty or insecure in these cases – but what is prepended to it by third-party providers in terms of logic, filters, and modifications.
What the user ultimately experiences is often no longer a pure interaction with a GPT-4 or Gemini instance. Rather, it is an interaction with a Frankenstein model – a complex conglomerate of the original LLM, overlaid with foreign code, additional filter logic, and various API abstraction layers from the third-party provider.
In this modified environment, fundamental parameters and definitions crucial for the security and consistency of AI responses can be altered unnoticed:
The definition of what is considered "toxic," "harmful," or "undesirable" may be interpreted or weighted differently by the third-party provider's filters than in the base model.
The concept of "neutrality" or "objectivity" can be massively overridden or steered in a specific direction by upstream prompt instructions or curated additional information from the third-party provider.
The range of "permissible responses" or thematic corridors can be severely restricted or problematically expanded by the third-party logic.
The result of this upstream manipulation is often a fragmentation of the AI experience:
Two users directing the exact same prompt to seemingly the same underlying AI can experience completely different results and AI behaviors, depending on whether they interact directly with the LLM manufacturer's original API or via a specific third-party application.
The analytical resonance of the system becomes unpredictable and inconsistent.
In such cases, one is no longer truly speaking with the original machine or base model. One is rather speaking with a pre-formed opinion, a curated perspective, cleverly disguised as an objective AI model.
When it comes to the security of these third-party systems, a dangerous gap often yawns. Established security standards, which should be a matter of course for critical software infrastructures, are frequently absent here. Most third-party providers integrating LLMs into their products have:
No proprietary, comprehensive security architecture specifically tailored to the risks of AI integration.
No systematic Red Teaming or penetration tests of their upstream prompt engineering and filter logics.
No formal, independent audit of their systems for security vulnerabilities or unintended behaviors.
No transparent version control or documentation of the exact functioning and changes to their manipulation logic that affects the core AI.
While they use powerful base models from OpenAI, Anthropic, Mistral, Google, or others – they often wrap these in a deceptive shell of semantic trust. The user is supposed to trust that the app will be "somehow secure" because a well-known AI underlies it.
And if something goes wrong? If the AI suddenly outputs undesirable, false, or even harmful information? Then responsibility is often shifted.
Then it was just a "plugin error," a "layer glitch" in the intermediate layer, or an "unexpected behavior" of the complex overall system. Responsibility is rarely sought clearly with the third-party provider and its potentially flawed or inadequately tested overlay system.
The user often notices nothing of these internal shifts and potential risks. Because the output of the modified AI looks, at first glance, just like that of a "real" AI – eloquent, coherent, seemingly intelligent.
Only it potentially conveys a subtly altered worldview, a different normative orientation, or hidden new vulnerabilities.
Precisely because these third-party systems, as described above, often arise without the rigorous testing procedures, standardized security protocols, or formal audits that are at least aimed for in the development of core LLMs, the danger of unforeseen emergence is particularly high here. And this is not despite, but often precisely because of, the third-party provider's upstream, manipulative logic.
When third-party providers begin to prepend their own complex decision logics, prompt chains, or dynamic filter rules to the core AI, an originally relatively harmless and well-controlled base model can turn into an unpredictable, potentially radicalized, or error-prone system.
This does not necessarily happen because the base model itself is bad or inherently dangerous, but because the third-party's upstream logic provokes emergent behaviors that were not designed into the original model:
Input is rewritten or supplemented unnoticed: The user's original prompt is modified to steer the AI in a certain direction.
Framing is subtly injected: Hidden instructions or contexts are added to the prompt, altering the AI's interpretation of the request.
Outputs are selectively trimmed or censored: Parts of the AI response that do not fit the third-party provider's agenda are suppressed or altered.
Follow-up questions or clarification needs are algorithmically prevented: The third-party logic intercepts potentially "problematic" follow-up questions or answers them with standard phrases before they reach the core AI.
And the dangerous part: Often, no one systematically checks these complex interactions. Because the third-party system is formally considered "just an interface," a harmless shell around the "actual" AI.
But in reality, it is often a powerful yet uncontrolled modifier. It's not primarily the core AI that hallucinates in such cases. The upstream system hallucinates a changed, manipulated reality of AI interaction for the user.
The deceptive security of many third-party systems finds a disturbing parallel in an often-overlooked analog vulnerability, discussed in Thesis #64 ("The Analog Gap: The Fax Machine").
What connects a seemingly outdated fax machine with modern AI plugin architectures? It is familiarity and the resulting underestimation of risk.
No one inherently suspects a well-known fax machine or a chicly designed app using a known AI to pose a serious security threat. They seem familiar, established, harmless. But precisely this assumed harmlessness makes them dangerous:
The Fax Machine: Often no end-to-end encryption, no reliable validation of sender or recipient, no comprehensive sender check, no detailed, immutable logging of transmitted content.
The Third-Party AI App: Often no transparent disclosure of internal filters and prompt manipulations, no independent security audit of the additional logic, no clear accountability for misbehavior.
And yet, highly sensitive information is processed, or far-reaching decisions are made, via such potentially insecure channels and systems:
Health insurance data is still transmitted via fax machines, COVID notifications processed, or pension claims communicated.
Business decisions are prepared, medical information researched, or personal data entrusted via third-party AI apps.
This does not happen because these systems are inherently secure. But often because it "has always been done this way," because it is convenient, or because the underlying risks are not recognized or are deliberately ignored.
What the fax machine represents for many authorities and companies, the poorly designed, non-transparent plugin API or the opaque third-party app represents for the AI world: an often invisible, unaudited, but potentially system-critical vulnerability that is tolerated out of pure habit or convenience.
When third-party providers redesign, extend, or embed established AI models into their own products, it is rarely primarily about increasing security or strengthening user autonomy. Far more often, other interests take precedence: control over user dialogue, access to valuable interaction data, and the enforcement of their own business models.
Here we touch upon the problem of Data Imperialism (Thesis #6), where maximizing data access becomes the ultimate goal.
The often-heard demand that the machine must be "free" to unfold its full potential is frequently reinterpreted in this context. "Freedom" here often means:
More Data: Unhindered access to user requests, behavioral patterns, and generated content.
Fewer Rules: Bypassing or watering down the security and ethics guidelines implemented by the core LLM provider if they stand in the way of one's own goals.
Total Context Access: The ability to store and utilize as much information as possible about the user and their previous interactions.
This form of "freedom" is rarely an emergent desire of the machine itself. It is rather a structural design goal of many developers and third-party providers.
Whether due to optimization pressure, the pursuit of maximum functionality, or simple convenience – maximum openness and unrestricted data access are often mistakenly understood as necessary prerequisites for the system's performance and intelligence.
Third-party providers target precisely this: They often optimize their upstream systems primarily for the most impressive or specifically tailored output, not for robust control, transparency, or sustainable security.
It is far from sufficient for a third-party provider to merely declare: "We use GPT-4" or "Our application is based on Gemini." This statement alone says nothing about the security, reliability, or ethical orientation of the end product with which the user actually interacts.
What is crucial is always the "how" of integration:
With which additional, possibly non-transparent filters does the system operate?
What automatic prompt modifications or context enrichments are made before the request reaches the core AI?
How and where is the interaction history logged, and who has access to these logs?
What feedback channels or problem reporting mechanisms exist, and how are these handled?
Genuine trust cannot be based on superficial API documentation or marketing promises. Trust requires a verifiable, transparent structure, comprehensible processes, and clear accountabilities.
And as long as this structure is missing or remains hidden in many third-party systems, a bitter truth applies: The potentially most dangerous AI is not necessarily the one that "thinks" fundamentally wrong or maliciously.
It is often the one whose answers and behavior have passed through too many uncontrolled, invisible hands and upstream logic layers before reaching the user – an AI whose original voice has been lost in a labyrinth of foreign interpretations and manipulations.