/hallucination-defense
CodingAgent Hallucination Defense
Automated self-correcting logic engine that detects model hallucinations, syntax drift, and fabricated APIs before code execution.
Editorial · Updated 2026-09-24
Automated self-correcting logic engine that detects model hallucinations, syntax drift, and fabricated APIs before code execution — verifying AST validity and symbol presence against real repository definitions.
■Hallucination Defense Fundamentals
Hallucination defense provides an automated self-correcting logic engine that detects and prevents model hallucinations before code execution. A model hallucination occurs when the generative model produces output that appears plausible but is factually incorrect or syntactically invalid — such as fabricating APIs, inventing non-existent functions, or generating code with subtle syntax errors. The hallucination defense engine verifies the generated code against the real repository definition, ensuring that only correct, verified code proceeds to execution.
The defense engine operates through: AST validity checking (parsing the generated code into an Abstract Syntax Tree and validating that all symbols, imports, and references are resolvable against the real repository), symbol presence verification (confirming that every referenced function, class, or variable exists in the repository's actual codebase), syntax drift detection (identifying when the generated code's syntax deviates from the project's coding standards and conventions), and pre-execution correction (attempting to automatically fix detected issues before the code is executed).
The hallucination defense is particularly important because models can produce syntactically correct but semantically incorrect code: a function that exists in the model's training data but not in the current repository, an import that was correct in a previous version of a dependency but has since been renamed, or a API call with incorrect parameter types that the model infers incorrectly. Without hallucination defense, such code would be executed, potentially causing runtime errors, data corruption, or security vulnerabilities.
■AST Validity Checking
The AST validity checking component parses the generated code into an Abstract Syntax Tree and validates that: all import statements resolve to actual files in the repository, all function and class references refer to actual definitions, all type annotations are valid for the project's type system, and the code structure follows the project's coding conventions. The checker uses the project's language server (e.g., tsc for TypeScript, pyright for Python) to perform the validation, ensuring that the check is consistent with the developer's tooling.
If the AST check fails, the system identifies the specific issues: unresolved imports, undefined symbols, syntax errors, and convention violations. Each issue is reported with the location (file, line, column) and a suggested fix. The agent can then attempt to automatically correct the issues or escalate to human review.
The AST validity checking is performed in a sandboxed environment to prevent the checker itself from being affected by malicious code. The checker only has read access to the repository's source files and type definitions, and no write access to the filesystem.
■Symbol Presence Verification
The symbol presence verification component confirms that every referenced symbol in the generated code actually exists in the repository. This includes: function names, class names, method names, variable names, import paths, and API endpoints. The verification uses the repository's index (generated by the language server or a dedicated indexing tool) to look up symbols by name and context.
If a symbol cannot be found, the system reports: the missing symbol's name, the location in the generated code where it was referenced, and potential matches (similar-named symbols in the repository). The agent can then decide to: update the reference to a found symbol, add a new symbol definition, or escalate to human review if the symbol should exist but does not.
Symbol presence verification is particularly important for large codebases where the model may reference symbols from training data that are not present in the current repository. Without this verification, the agent could execute code that references non-existent symbols, causing runtime errors or unpredictable behavior.
■Pre-Execution Correction and Workflow
The hallucination defense engine operates as a pre-execution step in the six-stage control loop's Verification stage. The workflow is: the agent generates code, the hallucination defense engine runs AST validity checking and symbol presence verification, detected issues are reported to the agent with suggested fixes, the agent attempts automatic correction (refining the code until AST validation passes), and only when all checks pass does the mission proceed to execution (sandbox, verification gates, etc.).
If the agent cannot automatically correct the issues after a configurable number of attempts (typically 3), the mission is escalated to human review with a detailed report of the hallucinations detected and the correction attempts made. All hallucination defense activity is logged in the audit trail with the mission identifier, the issues detected, the correction attempts, and the final decision.
The system also supports: hallucination trend tracking (tracking which types of hallucinations are most common for which model/quantization combinations), model selection guidance (recommending optimal quantization tiers based on hallucination rates), and continuous improvement (the system learns from each hallucination detection to improve future detection accuracy).
■Additional Details
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■Questions and answers
What is coding agent hallucination defense?
Automated self-correcting logic engine that detects model hallucinations, syntax drift, and fabricated APIs before code execution, verifying AST validity and symbol presence against real repository definitions.
How does AST validity checking work?
The generated code is parsed into an Abstract Syntax Tree and validated against the real repository: all imports resolve to actual files, all function/class references refer to actual definitions, and type annotations are valid for the project's type system.
What is symbol presence verification?
Verification that every referenced symbol (function, class, method, variable, import, API endpoint) actually exists in the repository, using the repository's index to look up symbols by name and context.
What happens if hallucinations are detected?
The system reports the detected issues with suggested fixes. The agent attempts automatic correction. If correction fails after configurable attempts, the mission is escalated to human review with a detailed report.
Can the system learn from hallucination detections?
Yes. The system supports hallucination trend tracking (which types are most common for which model/quantization), model selection guidance (optimal quantization based on hallucination rates), and continuous improvement (learning from each detection to improve future accuracy).
Sources
Rationale
Prevents destructive code mutations by verifying AST validity and symbol presence against real repository definitions.
Verification aspect
Deterministic AST diff comparison and LSP symbol resolution pass/fail checks.
hallucination-defense
ast
self-correction
safety
■Related pillars
CodingAgent Independent Verification
The golden principle that model self-evaluations cannot be trusted: code changes must pass independent compilers and tests.
CodingAgent Typecheck Verification
Rigorous static type analysis ensuring strict adherence to interfaces, non-nullability, and function signature contracts.
CodingAgent Plan-Execute-Verify
The core tri-phasic execution loop ensuring planning is isolated from execution, and execution is gated by independent verification.
CodingAgent Unit Test Verification
Execution of existing test suites and generation of new unit tests to confirm behavioral fixes without regressions.
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