Ghosts in the Machine: The Rise of Hidden AI on Social Media

Photo by Gwendal Cottin on Unsplash

Earlier today, I found myself scrolling through my X feed when I encountered a peculiar reply. A user called "PUNISHER" was tweeting at me about deep state conspiracy theories involving Elon Musk and Donald Trump.

Nothing remarkable there, as such content has become commonplace. What caught my attention was the unnatural cadence of the reply, the peculiar way the account engaged–or didn’t–with others, and its strategic deployment of politically charged language.

After many years of studying information security and learning a bit on the side about computational linguistics, I've developed what my colleagues jokingly call a "bot detector" in my brain. Something about this account immediately felt off—not quite human.

I decided to investigate further, and to try to trigger it to reply to me directly.

"The distinction between human and artificial intelligence is becoming increasingly blurred online," I noted in my research journal. "What we're seeing aren’t chatbots labeled as such, but secretive AI masquerading as community citizens in our digital town squares… leading your average social media user to imagine those bots, with their bizarre ideologies and hate speech, as their human friends and neighbors."

The Invisible Infiltration

The account I had stumbled upon displayed several telltale signs of being an AI language model (LLM) rather than a human user. Its responses showed consistency in tone and style across hundreds of posts. It engaged with multiple users simultaneously without the natural breaks we expect from human users. Most tellingly, when challenged with conceptually complex questions that required genuine understanding rather than pattern matching, its responses became formulaic and erratic.

After a fruitless back-and-forth in which I attempted to “jailbreak” the LLM, the presumably-human operator of this particular “herd” of fake accounts detected my attempts and decided to block me without further ado.

These systems are designed to be persuasive.

LLMs mimic human communication patterns with fairly good accuracy, but lack genuine understanding. These are essentially sophisticated pattern-matching systems trained on vast datasets of human language. Having identified this artificial presence, I began to catalog the specific patterns that revealed its non-human nature, which are clues any vigilant user can employ in their own online interactions.

Detecting the Ghosts Among Us

The account's posts strategically amplified particular narratives about "deep state" conspiracies, using language calibrated to resonate with specific ideological viewpoints. It shifted between appearing reasonable to new audiences and reinforcing more extreme positions when engaging with already-convinced followers. For the average user, detecting hidden AI through purely non-technical means remains challenging, but feasible.

Consider these patterns I observed in my case study:

1. Unnatural responsiveness: The account replied to me immediately with no significant breaks, and could be observed responding to various users with political replies which are only 2-3 minutes apart.

2. Contextual inconsistencies: When challenged on specifics, the account would pivot to new topics rather than acknowledge confusion.

3. Formulaic emotional expressions: The account's expressions of outrage or concern follow predictable patterns that lack the messy spontaneity of genuine human emotion.

4. Strategic ambiguity: When confronted with direct questions that might reveal its nature, the account consistently deployed vague language and redirections.

This is a bit like learning to spot a forgery: once you know what patterns to look for, they become increasingly obvious, though never perfectly reliable. Understanding how to identify these AI systems with the naked eye leads naturally to the question of how they are created and deployed in the first place.

The Technical Reality

From a technical perspective, the deployment of these hidden AI systems is surprisingly straightforward. Modern language models can be fine-tuned on specific ideological content, customized to mimic particular writing styles, and programmed to pursue strategic communication objectives.

It's relatively simple to create what researchers call a "persona shell" around an LLM. You provide the system with a backstory—in this case, "ex-military from West Virginia"—and some example content that matches the desired communication style. The system then maintains that persona consistently across thousands of interactions.

The economic barriers to entry for deploying such systems have collapsed. What would have required millions of dollars and a team of specialized engineers just three years ago can now be accomplished by a single operator with modest technical skills and a few thousand dollars. With such technology now widely accessible, we must confront the broader implications of its proliferation across our digital ecosystems.

Hidden Dangers, Real Consequences

The proliferation of hidden AI on social platforms presents several concerning implications for our digital ecosystems.

First, these systems can dramatically amplify the scale and reach of influence operations. A single operator can deploy dozens or hundreds of AI personas across platforms, each appearing to be independent human users with consistent backstories, linguistic patterns, and viewpoints.

Second, they undermine the fundamental assumption of human agency in online discourse. When we engage with content online, we typically assume we're interacting with other humans operating in good faith. That social contract is being violated in ways most users aren't equipped to detect.

Perhaps most troublingly, these systems can learn from engagement. Each interaction provides valuable data that helps refine future messaging. Many of the AI personas I’ve studied appeared to strategically modify their rhetoric based on which messages received the most engagement, gradually optimizing for maximum impact.

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While my encounter with the "PUNISHER" account reveals the presence of hidden AI in social media, this phenomenon extends far beyond public platforms into the seemingly private realm of apps we use daily.

The Bigger Picture

It's a mundane Thursday morning. You snap a photo of your breakfast, share your location with a friend, and maybe check tomorrow's weather—all through apps on your smartphone. What you likely don't realize is that in approximately one out of every four of these digital interactions, an unseen data exchange is taking place. Your personal information—ranging from innocuous preferences to intimate details—is being quietly transmitted to AI systems such as ChatGPT, Claude, Gemini, or Perplexity.

The machinery behind this hidden data collection operates in two distinct fashions, each with its own particular set of concerns.

• When you use an app that employs cloud-based AI, your information travels across the internet to distant servers where the actual processing occurs. Imagine taking a selfie with an image-enhancement app; the photograph might journey to a remote data center, bringing along a trail of metadata about your device, location, and usage patterns. This digital exodus creates a moment of vulnerability, as your personal information, now outside the relative "sanctuary of your device, becomes susceptible to interception, unauthorized storage, or repurposing.

• The alternative approach keeps AI processing local, embedding the algorithms directly within the app on your device. While this method may seem preferable from a privacy perspective because your data stays put, it introduces its own complications. These on-device AI models might be outdated versions vulnerable to exploitation, or they could have been implemented without proper licensing from their creators, raising both security and ethical questions.

The technical infrastructure enabling these hidden AI systems has several potential points of failure.

• Many apps connect to AI services using what software developers call API keys—essentially “digital passkeys” that grant access to sophisticated AI tools. When these credentials aren't adequately protected, which is super common, they become targets for malicious actors who can appropriate them to exploit AI services at the developing company's expense, potentially accessing processed data along the way–with the staff at the company proceeding with business as usual as they remain none the wiser to the breach.

• Some applications include pre-packaged AI models—essentially bundles of code that enable the app to make predictions or decisions. When these packages aren't regularly updated or originate from questionable sources, they can harbor security vulnerabilities or be manipulated to behave in ways the app developer never intended.

• Perhaps most concerning are the endpoint URLs—the internet addresses where your data gets dispatched for AI processing. Connections to established services from tech giants like Amazon or Microsoft carry some presumption of security protocols and oversight. However, many apps establish links with lesser-known services that operate with minimal security safeguards and have ambiguous privacy policies.

The consequences of this clandestine AI usage extend beyond abstract privacy concerns. When apps surreptitiously process your data through AI systems, the potential implications range from moderate to severe. Your personal information (photographs, messages, menstrual data, location history, etc…) might be analyzed by artificial intelligence without explicit consent. Financial exposure becomes a risk if hackers compromise an app's AI access credentials, potentially generating enormous costs for the company that could affect service continuity. Data breaches become more likely when the AI service or its connection points lack robust security measures. And as governments worldwide seek to implement increasingly stringent regulations concerning AI usage, applications that fail to disclose their AI features transparently face potential legal repercussions, which could disrupt services users have come to rely upon.

What emerges from this technical landscape is a realization that many everyday applications leverage sophisticated artificial intelligence capabilities in ways that remain largely invisible to users. While these hidden AI systems enable convenient features and enhanced functionality, they simultaneously introduce novel privacy and security concerns that most consumers remain unaware of. The companies developing these applications bear responsibility for greater transparency regarding their AI implementations and for implementing appropriate security measures to safeguard user data.

In the European Union, the AI Act now requires clear disclosure when users interact with AI systems. Similar legislation has been proposed in the United States, though its passage remains unlikely in the current political climate. In the absence of comprehensive regulation, one uncomfortable truth becomes apparent: the ostensibly "free" applications on our smartphones might actually extract a hidden cost—our personal information being harnessed to train or enhance AI systems without our knowledge or consent.

Returning to the social media landscape, the responsibility for addressing these hidden AI systems ultimately falls to the platforms themselves, though their incentives do not align with transparent enforcement.

Platform Responsibility and Political Dimensions

Platform owners like X have the technical capability to detect and label these AI systems which abuse their Terms of Service. Sophisticated pattern analysis, behavioral fingerprinting, and neural linguistic markers can identify non-human actors with reasonable accuracy. However, implementing such measures requires both resources and, crucially, the will to act. There is an inherent tension between platform growth metrics and aggressive enforcement against inauthentic behavior. Artificially amplified engagement still counts as engagement in quarterly reports to shareholders.

In the case of X specifically, the platform's ownership has made explicit statements embracing political positions that align with many of the narratives being amplified by these hidden AI systems. This creates a conflict of interest that complicates enforcement decisions and users’ perceptions of the same.

During my analysis of the "PUNISHER" account, I observed how the AI strategically deployed political conspiracy theories about the "deep state" while maintaining plausible deniability through careful linguistic hedging. The account's content consistently aligned with specific ideological viewpoints favored by the platform's ownership. My experience with this single account which I provided here as a small case study reflects a much larger pattern of AI deployment that will only grow more sophisticated as the technology advances.

The Road Ahead

As AI systems continue to advance, the line between human and artificial communication will further blur. Platform owners face growing pressure to implement transparent disclosure requirements for AI-generated content.

This isn't about prohibiting AI participation in online spaces. It's about informed consent and transparency. Users deserve to know whether they're engaging with a human or an artificial system.

Until comprehensive regulation is implemented, users must develop their own critical literacy skills for this new landscape. We need to approach online interactions with a healthy awareness that some participants may not be what they appear.

The town square still offers valuable connection and discourse, but we would be wise to remember that not all voices within it belong to fellow humans. Some are merely ghosts in the machine, programmed to persuade, deployed to influence, and designed to be invisible—until you know exactly what to look for.