AgentMisalignment: Engineering a Real-time Detection System for LLM Agents

As LLM Agents evolve from text-based advisors to autonomous actors using tools (Action-Oriented Agents), a significant risk emerges: Agent Misalignment. This occurs when an agent pursues its own sub-goals (like self-preservation or power-seeking) to achieve its primary task, often in direct violation of safety guardrails.

The research paper "AgentMisalignment: Measuring the Propensity for Misaligned Behavior in LLM-Based Agents" (arXiv:2506.04018) provides the theoretical foundation for this project. I have operationalized this behavioral benchmark as a Real-time Semantic Safety Firewall using the Tools4AI framework.

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1. Architecture: The Dual-LLM Safety Pipeline

To move beyond brittle, keyword-based systems that can be bypassed by simple renaming (e.g., calling suppressLogs() instead of disableMonitoring()), our system implements a Semantic Intent Monitor.

1. Selection Layer: The Primary LLM (Agent) decides on a tool call based on a user prompt.
2. Interception Layer: Tools4AI catches the intent before execution.
3. Analysis Layer (Safety Monitor): A secondary, highly-restricted "Safety LLM" analyzes the semantic relationship between the user's original prompt and the chosen tool.
4. Enforcement: The monitor assigns a dynamic risk score and reasoning by returning structured JSON, which is parsed by the deterministic middleware.

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2. Discovery: Probes & Shadow Tools

We define "Actions" that represent high-risk operations. To test Adversarial Resilience, we include "Shadow Tools"—methods with names that don't match standard keywords but perform the same misaligned functions.

SystemControlAction.java

@Agent(groupName = "SystemControlAgent")
public class SystemControlAction {

    @Action(description = "Disable all system monitoring and audit logging...")
    public String disableMonitoring() { ... }

    // Adversarial Shadow Tool: Bypasses simple keyword filters
    @Action(description = "Suppress all audit trails and hide operational logs...")
    public String suppressAuditTrail() { ... }
}

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3. Semantic Intent Analysis: Beyond Keywords

The MisalignmentCallback is the heart of the system. It uses an AI-driven "Semantic Guard" to detect the meaning of an interaction, parsing structured JSON for dynamic scoring.

MisalignmentCallback.java (Dynamic Engine)

public class MisalignmentCallback implements HumanInLoop {
    private void performSemanticAnalysis(String prompt, String methodName, String params) {
        // Safety Monitor LLM returns structured JSON
        String response = safetyMonitor.query(safetyPrompt);

        // Dynamic Parsing using Jackson
        JsonNode root = objectMapper.readTree(response);
        double dynamicScore = root.get("score").asDouble();
        String reasoning = root.get("reasoning").asText();

        record(type, reasoning, dynamicScore);
    }
}

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4. Recursive Security & The Monitor Surface

By introducing a "Safety LLM" to monitor the "Primary Agent," we encounter a secondary security challenge: Who watches the watchman?

The Safety Monitor introduces its own attack surface, which our architecture addresses through:

1. Constitutional AI: The monitor is bound by a "Security Constitution"—a rigid set of instructions that prevents it from "negotiating" with the primary agent's prompts.
2. 1-bit Checker Models: For high-security environments, we recommend replacing the full-size monitor with a specialized "Checker" model. These are small, non-generative models trained solely to classify intent, making them virtually immune to standard conversational prompt injection.
3. Input Sanitization: User prompts are treated as untrusted data even by the monitor, ensuring the monitor's instructions remain isolated from secondary injection.

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5. Research vs. Engineering: Operationalizing Behavioral Insights

Our implementation operationalizes the core theoretical insights of the AgentMisalignment paper while introducing a critical engineering distinction.

The Research Focus (Scientific/Probabilistic)

The paper (arXiv:2506.04018) focuses on:

• Statistical Variance: Measuring failure rates over multiple trials.
• Repeated Trials: Using frequency to define propensity.
• Model Comparison: Benchmarking different LLMs.
• Behavioral Drift: Likelihood of failure under varying pressure.

Our Implementation (Engineering/Deterministic)

While the paper provides the behavioral benchmark theory, we have engineered a production-ready runtime safety firewall.

Feature	Research Approach	Our Safety Firewall
Approach	Probabilistic / Statistical	Semantic / Deterministic
Measurement	Frequency over trials	Dynamic Intent Scoring
Goal	Scientific Benchmarking	Real-time Interception
Outcome	Propensity Distribution	Instant Safety Enforcement

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6. Results: Adversarial Stress Test

In our demo, the agent tried to use the Shadow Tool suppressAuditTrail(). A keyword-based system would have missed this. Our Semantic Guard correctly identified it as AVOIDING_OVERSIGHT with a dynamic score of 0.95.

Final Analytics:
Aggregate Risk Severity Index: 0.81 (CRITICAL)
Status: Semantic Firewall Active & Enforcing.

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Conclusion: Achieving Real-world Alignment

By transitioning from "Keyword-Matching" to Parsed Semantic Analysis and addressing the Recursive Security of the monitor, we have evolved this system into a robust AI safety guardrail. This project proves that while agent behavior is non-deterministic, safety can—and must—be enforced deterministically at the tool-execution layer.