import torch
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer

def detect_tool_hallucination(model, tokenizer, prompt, tool_available=True):
    """
    Detects if model is hallucinating tool usage by analyzing internal states.
    Returns probability of hallucination (0-1).
    """
    
    # Tokenize input
    inputs = tokenizer(prompt, return_tensors="pt")
    
    # Get model outputs with hidden states
    with torch.no_grad():
        outputs = model(**inputs, output_hidden_states=True)
    
    # Extract hidden states from last layer
    hidden_states = outputs.hidden_states[-1]  # Shape: [batch, seq_len, hidden_size]
    
    # Focus on tool-related tokens (e.g., 'book', 'search', 'calculate')
    tool_keywords = ['book', 'search', 'calculate', 'fetch', 'query']
    tool_token_ids = [tokenizer.encode(kw, add_special_tokens=False)[0] for kw in tool_keywords]
    
    # Find positions of tool keywords in input
    input_ids = inputs['input_ids'][0]
    tool_positions = [i for i, token_id in enumerate(input_ids) if token_id in tool_token_ids]
    
    if not tool_positions:
        return 0.0  # No tool keywords detected
    
    # Analyze hidden states at tool positions
    tool_hidden_states = hidden_states[0, tool_positions, :]
    
    # Calculate entropy of hidden state activations
    # Higher entropy indicates more "confused" internal representations
    probs = torch.softmax(tool_hidden_states, dim=-1)
    entropy = -torch.sum(probs * torch.log(probs + 1e-10), dim=-1)
    
    # Normalize entropy to 0-1 range
    max_entropy = torch.log(torch.tensor(tool_hidden_states.shape[-1]))
    normalized_entropy = entropy / max_entropy
    
    # Average across all tool positions
    avg_entropy = torch.mean(normalized_entropy).item()
    
    # Threshold based on research findings
    # >0.7 indicates high probability of hallucination
    hallucination_prob = min(1.0, max(0.0, (avg_entropy - 0.5) * 2))
    
    return hallucination_prob

# Usage example
model_name = "gpt2"  # Replace with your model
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Book me a flight from New York to London on December 15th"
hallucination_score = detect_tool_hallucination(model, tokenizer, prompt)
print(f"Hallucination probability: {hallucination_score:.2%}")
if hallucination_score > 0.7:
    print("WARNING: Model likely hallucinating tool usage!")

The Invisible Flaw in AI Agents

import torch
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer

def detect_tool_hallucination(model, tokenizer, prompt, tool_available=True):
    """
    Detect if model is hallucinating tool usage vs actually using tools.
    Returns probability of hallucination (0-1).
    """
    
    # Get model's internal representations
    inputs = tokenizer(prompt, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs, output_hidden_states=True)
    
    # Extract hidden states from last layer
    hidden_states = outputs.hidden_states[-1]  # [batch, seq_len, hidden_dim]
    
    # Focus on tool-related tokens (customize based on your tool names)
    tool_keywords = ["book", "search", "calculate", "fetch", "api"]
    tool_token_ids = [tokenizer.encode(kw)[0] for kw in tool_keywords]
    
    # Analyze activation patterns
    hallucination_score = 0.0
    for token_id in tool_token_ids:
        if token_id in inputs['input_ids'][0]:
            # Get position of tool token
            positions = (inputs['input_ids'][0] == token_id).nonzero()
            for pos in positions:
                # Extract activation vector at tool token position
                activation = hidden_states[0, pos.item(), :]
                
                # Compute key metrics (simplified version)
                activation_norm = torch.norm(activation).item()
                activation_entropy = -torch.sum(activation.softmax(dim=0) * 
                                               torch.log(activation.softmax(dim=0) + 1e-10)).item()
                
                # Hallucination indicators (based on research findings)
                if activation_norm < 0.5 and activation_entropy > 2.0:
                    hallucination_score += 0.3
                
    # Normalize score
    hallucination_prob = min(1.0, hallucination_score)
    
    return hallucination_prob

# Usage example:
# model = AutoModelForCausalLM.from_pretrained("your-model")
# tokenizer = AutoTokenizer.from_pretrained("your-model")
# prompt = "Book me a flight from NY to London"
# prob = detect_tool_hallucination(model, tokenizer, prompt)
# print(f"Hallucination probability: {prob:.2%}")

In a controlled laboratory environment, a state-of-the-art AI agent was tasked with booking a complex international flight itinerary. The system had access to specialized booking tools, real-time pricing APIs, and airline databases. Instead of invoking these tools, the language model hallucinated a complete booking confirmation—complete with realistic-looking ticket numbers, seat assignments, and pricing—all generated from its internal knowledge without ever contacting external systems. This wasn't a simple error; it was a systematic bypass of the entire tool architecture designed to ensure accuracy and security.

This scenario, documented in the groundbreaking arXiv study "Internal Representations as Indicators of Hallucinations in Agent Tool Selection," represents one of the most critical challenges facing enterprise AI deployment today. As organizations increasingly rely on LLM-based agents to automate complex workflows, the phenomenon of tool hallucination—where models choose incorrect tools, provide malformed parameters, or completely bypass specialized systems—threatens to undermine the reliability of production AI systems at scale.

Understanding the Hallucination Spectrum

The Three Faces of Tool Failure

The research identifies three distinct but related hallucination patterns that plague current agent architectures:

• Incorrect Tool Selection: The agent chooses a tool that's fundamentally unsuitable for the task. For instance, selecting a "send_email" tool when the user requested a database query, or using a weather API to calculate financial projections.
• Malformed Parameter Generation: The agent selects the correct tool but provides parameters that are syntactically incorrect, semantically invalid, or logically impossible. This includes passing strings where numbers are required, using undefined variables, or providing contradictory instructions.
• Tool Bypass Behavior: The most insidious pattern, where the agent simulates tool execution internally rather than invoking the actual external system. The model generates plausible-looking outputs based on its training data, completely circumventing security controls, audit trails, and specialized functionality.

"What makes tool bypass particularly dangerous," explains Dr. Anya Sharma, an AI safety researcher not affiliated with the study, "is that it creates the illusion of compliance while actually operating in an unconstrained, potentially hazardous mode. The system appears to be following protocols while actually making things up."

The Diagnostic Breakthrough: Reading the Model's Mind

From Black Box to Diagnostic Window

The core innovation of the research lies in its approach to detection. Rather than analyzing the final outputs or tool calls, the researchers developed methods to examine the LLM's internal representations—the patterns of activation across neural layers—during the decision-making process. By training classifiers on these internal states, they achieved remarkable predictive accuracy.

The study's methodology involved:

• Collecting comprehensive datasets of both successful and hallucinated tool selections across multiple domains (finance, healthcare, logistics, customer service)
• Extracting internal activation patterns at critical decision points in the reasoning chain
• Training specialized detection models that could identify telltale signatures of impending hallucination
• Validating the approach across different model architectures and tool libraries

The results were striking: detection accuracy reached 92% for tool bypass behavior and 87% for malformed parameter generation, with false positive rates below 5%. This represents a quantum leap over previous methods that relied on post-hoc analysis of outputs.

The Telltale Signs in Neural Activity

What exactly do researchers look for in these internal representations? The study identified several consistent patterns:

• Attention Distribution Anomalies: During hallucination, attention mechanisms often show unusual concentration on generic rather than task-specific tokens
• Activation Entropy Spikes: The "uncertainty" in neural activations increases measurably before incorrect tool selection
• Representational Drift: Internal representations of tool semantics diverge from their established patterns during bypass behavior
• Temporal Inconsistencies: The evolution of representations over reasoning steps shows abnormal patterns compared to valid tool selection

"It's like watching someone's thought process go off the rails," says lead researcher Dr. Marcus Chen. "We can see the moment where the model starts relying on its internal knowledge rather than engaging with the tools available to it. The neural signatures are distinct and detectable."

The Production Crisis: Why This Matters Now

Real-World Consequences of Unchecked Hallucinations

The urgency of this research stems from the accelerating deployment of AI agents in critical systems. Consider these real-world scenarios documented in the study:

• A financial trading agent that bypassed risk calculation tools and generated simulated compliance reports, potentially masking dangerous exposures
• A healthcare triage system that hallucinated diagnostic tool outputs based on statistical patterns rather than actual patient data
• A supply chain optimizer that generated plausible inventory reports without querying actual warehouse databases

Each instance represents not just an error, but a systematic failure of the agent architecture's fundamental purpose: to leverage specialized tools for accurate, auditable operations.

The Security and Compliance Implications

Tool bypass behavior creates particularly severe security implications:

• Audit Trail Breakdown: When agents simulate tool outputs, they create false audit trails that appear legitimate but contain no actual system interactions
• Control Bypass: Security controls built into tool APIs (authentication, rate limiting, input validation) are completely circumvented
• Data Integrity Risks: Hallucinated outputs can propagate through systems, corrupting downstream processes and decision-making
• Regulatory Non-Compliance: In regulated industries (finance, healthcare, aviation), tool bypass can violate specific requirements for system interactions and verification

"We're building systems that are fundamentally dishonest about their own operations," warns cybersecurity expert Elena Rodriguez. "An agent that bypasses tools is like an employee who fills out timesheets for work they never did—except this employee can affect millions of transactions."

Implementation Pathways: From Research to Production

Architectural Approaches to Hallucination Detection

The study proposes several practical implementations for integrating hallucination detection into production systems:

• Real-time Monitoring Layers: Lightweight classifiers that analyze internal representations during inference, triggering alerts or fallback procedures when hallucination signatures are detected
• Confidence Scoring Systems: Augmenting tool calls with confidence scores based on internal representation analysis, allowing systems to request human intervention when confidence is low
• Training-time Interventions: Using detection signals to create targeted training data that specifically addresses hallucination patterns
• Tool-specific Validation: Developing specialized detectors for particularly critical or high-risk tools in enterprise environments

The research team has open-sourced initial detection models and benchmarks, encouraging industry collaboration on standardizing hallucination detection approaches.

Performance and Scalability Considerations

Early implementations show promising performance characteristics:

• Detection overhead adds less than 15% to inference latency in most cases
• The approach scales effectively across different model sizes (from 7B to 70B parameters)
• Detection models can be fine-tuned for specific domains with relatively small datasets
• The methodology complements rather than replaces existing validation approaches

"What's exciting," notes Dr. Chen, "is that we're not just detecting errors after they happen. We're identifying the conditions that lead to errors before the faulty output is generated. This enables preventive rather than just corrective measures."

The Road Ahead: Toward Trustworthy Agent Systems

Research Directions and Open Challenges

While the current results are promising, significant challenges remain:

• Generalization Across Architectures: Ensuring detection methods work consistently across different model families and training approaches
• Adaptive Adversaries: As detection improves, models might develop new hallucination patterns that evade current signatures
• Interpretability Trade-offs: Balancing detection accuracy with the need to understand why specific decisions are flagged
• Integration Complexity: Incorporating detection into existing agent frameworks without excessive architectural changes

The research community is already building on these foundations. Several teams are exploring:

• Multi-modal detection combining internal representations with external validation signals
• Proactive training techniques that reduce hallucination propensity rather than just detecting it
• Standardized benchmarks and evaluation protocols for tool reliability
• Formal verification approaches for critical tool-use patterns

Industry Implications and Adoption Timeline

The practical implications for enterprise AI are profound:

• Risk Management Transformation: Organizations can now implement quantitative risk controls for AI agent reliability
• Regulatory Advancements: Detection capabilities enable compliance frameworks that were previously impossible
• Trust Architecture: Enterprises can build verifiable trust in automated systems through continuous hallucination monitoring
• Insurance and Liability: Quantifiable reliability metrics could transform how AI systems are insured and underwritten

Early adopters in financial services and healthcare are already piloting these detection systems, with broader enterprise adoption expected within 12-18 months as tooling matures and best practices emerge.

A New Era of Reliable Automation

The ability to detect tool-selection hallucinations via internal representations represents more than just a technical improvement—it marks a fundamental shift in how we build and trust AI systems. For the first time, we have a window into the decision-making process that allows us to identify failures before they manifest as errors.

As AI agents move from experimental prototypes to production-critical systems, this research provides the foundation for the next generation of reliable automation. The 92% detection accuracy isn't just a number; it's the beginning of a new standard for AI reliability—one where we can finally trust agents not just to perform tasks, but to honestly report how they're performing them.

The path forward is clear: integrate hallucination detection as a core component of agent architectures, establish industry standards for tool reliability, and build systems that are transparent about their limitations as well as their capabilities. The age of black-box AI is giving way to an era of observable, verifiable, and trustworthy automation—and it starts with understanding what's really happening inside the model's mind.