Ghost in the AI Models — Part 2

Unseen Vulnerabilities

When I wrote about “Ghosts in the AI Models” on May 2023, the landscape of artificial intelligence was turbulent, yet it feels quaint compared to the currents we face now. Since that time, advancements like multimodal LLMs, hyperparameter-efficient training, and agent orchestration have raised the stakes. New vulnerabilities have also surfaced, many of which are still poorly understood or entirely invisible to the wider AI community.

This isn’t surprising. The emergent complexity of AI systems is similar to the phenomena I described in earlier article, that small, distributed errors acting across vast high-dimensional spaces, quietly accumulating until they erupt into something catastrophic. Let’s take this further, grounding it mathematically and connecting it to recent developments.

Cumulative Latent Drift and High-Dimensional Fragility

In high-dimensional vector spaces, where AI models like LLMs operate, every parameter contributes minutely to the model’s behavior. These parameters exist in a coupled system where the relationship between variables is non-linear and subject to feedback loops. The cumulative effect of minor errors in such systems, what I termed “polygenic ghosts” in part-1, is not merely hypothetical. It is intrinsic.

Consider a loss function L(θ) optimized over n-dimensional parameters θ:

Each fi(θi) represents a localized contribution to the loss. When dimensionality n is large, the individual contributions fi(θi) may seem insignificant, but their aggregate impact becomes a summation over small-magnitude noise across n components. For n→10¹² (typical in large transformer models), the system becomes hypersensitive to distributed noise, a phenomenon well-studied in high-dimensional statistics and optimization theory.

This is where things break down. Numerical stability in machine learning hinges on assumptions about local smoothness and the absence of adversarially accumulated drift across dimensions. But what happens when these assumptions are violated?

From Data Poisoning to Function Call Chaining

The problem isn’t just about distributed errors. It’s about new classes of vulnerabilities emerging at intersections of modular systems, areas still neglected in mainstream discussions. Here’s a concept called Data Poisoning During Chaining through Function Calls:

Current LLMs have enabled function calling to access external APIs, perform computations, or fetch live data. Maliciously poisoned datasets can exploit these calls by injecting subtle dependencies across functions. The result is a chained attack that exploits the LLM’s interpretive steps during multi-hop reasoning.

1. A poisoned data point subtly modifies the weights of a model.
2. When the model performs an API call, it misinterprets the returned data, cascading errors into downstream decision trees.
3. In subsequent iterations, the model’s retraining process amplifies the initial corruption.

This chaining vulnerability is orthogonal to traditional adversarial attacks because it exploits the interaction between data ingestion and functional interpretive steps, a pathway that can bypasses current security frameworks for AI.

The Role of Multi-Agent Coordination in Error Accumulation

Another evolution since my last post is the rise of agents orchestrated via LLMs. These agents act within complex systems, collaborating and competing to achieve tasks. The problem? Their interactions exacerbate the very vulnerabilities I’ve described.

In systems like AutoGPT and OpenAgent, autonomous agents communicate using shared embeddings, constrained by finite representational capacity. Here’s where latent drift propagation manifests. Mathematically:

1. Let agent A output an embedding v ∈ R^d representing its decision.
2. Another agent B updates its state vector u based on v: u′ = u + αv
3. As n agents interact over t time steps, we observe error amplification:

For large n, even small errors in v_i​ propagate multiplicatively, leading to runaway divergence. This latent drift across multi-agent systems mirrors the cascading fragility observed in network theory.

Guardrails vs Testing Immunity

Since GPT-4, AI labs have touted advancements in robust fine-tuning and adversarial evaluations. Techniques like:

• Contrastive tuning to identify counterfactual reasoning errors
• Negative sampling to stress-test edge cases
• Chain-of-thought prompts to improve reasoning fidelity

These help, but they are stopgaps. A concerning trend is testing immunity, where repeated adversarial tuning drives errors into dimensions not explicitly tested. These errors reappear later, disguised as emergent behaviors. This resembles hidden variable dependencies in causal inference, where unobserved confounders undermine intervention efficacy.

Mathematically, testing immunity can be modeled as a shift in error distribution p(e)p(e)p(e):

1. Initially: e ∼ N(0, σ^2)
2. Post-testing: e ∼ N(μ, σ^2/k), where k is the number of testing iterations.
3. Emergent phase: Errors reappear as e ∼ C(λ) with C a heavy-tailed Cauchy distribution.

This re-emergence of errors in poorly tested areas creates “robust polygenic ghosts,” making further testing exponentially harder.

The Arms Race: Doubling Down Again

So, where do we stand? In my previous post, I argued for doubling down on AI development to expose weak ghosts. Today, I remain convinced, but with refinements:

Differential Systems Testing: Instead of testing a single robust model, we need ensembles of weak models tuned to specific vulnerabilities. These models can serve as “probes” to identify polygenic vulnerabilities across the shared vector space.

Adversarial Multi-Agent Frameworks: Using agent-based simulations to amplify failure cascades under controlled conditions can help identify vulnerabilities in inter-agent communication and planning.

Error Budgeting in Large-Scale Systems: Borrowing from SRE (Site Reliability Engineering), error budgets should quantify acceptable levels of emergent vulnerabilities. Mathematically:

Here, fi(ei) measures the amplification of error e_i​ within the system, and E_b​ sets thresholds for intervention.

Regulating Open API Access: Unrestricted API endpoints amplify vulnerabilities like Data Poisoning during call chains. A shift toward controlled test environments with sandboxed access is non-negotiable.

Toward Controlled Chaos

Humanity stands at a critical juncture ever since we have hit the exponential curve. The development of AI systems capable of reasoning, planning, and interacting autonomously will only accelerate. Pausing isn’t the answer, nor is blind acceleration. The solution lies in recognizing and exploiting vulnerabilities before they metastasize.

The mathematical frameworks for addressing these challenges , such as Bayesian modeling, causal inference, and multi-agent dynamical systems do exist. But without coordinated efforts to focus on weak ghost detection, we risk letting these systems outpace our ability to understand or contain them.

The ghosts are here. They are multiplying. The real question is whether we have the resolve and foresight to face them head-on.