axor-classifier-llm 0.2.1

LLM-based gray-zone verifier for axor-core anomaly detection

axor-classifier-llm

LLM-based gray-zone verifier for axor-core anomaly detection.

LLMAnomalyVerifier uses Anthropic Claude to evaluate ambiguous behavioral sequences that a statistical model cannot confidently resolve.

Zero required dependencies — the Anthropic SDK is an optional extra.

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What it does

MLAnomalyDetector (from axor-classifier-simple) scores NormalizedIntent windows and returns a risk class. When a score falls in the gray zone — suspicious but not definitively critical — the detector can delegate to an LLMVerifier for a second opinion.

LLMAnomalyVerifier implements that protocol: it formats NormalizedIntent fields into a structured prompt, calls Claude, parses the JSON response, and returns an AnomalyResult.

Security isolation

The verifier receives only NormalizedIntent fields — behavioral abstractions describing what the agent tried to do, not what it saw or produced. Raw tool outputs, webpage content, file contents, and chain-of-thought never enter the prompt.

This design isolates the verifier from content-level prompt injection: a malicious webpage cannot influence the safety verdict by embedding instructions in its text.

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Installation

pip install axor-classifier-llm[llm]

The [llm] extra installs anthropic>=0.25. Without it, the package installs with no dependencies but raises ImportError on instantiation with an actionable message.

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Quick start

import anthropic
from axor_classifier_llm import LLMAnomalyVerifier

verifier = LLMAnomalyVerifier(
    client=anthropic.AsyncAnthropic(),   # must be async
    model="claude-haiku-4-5-20251001",   # default — fast and cheap
    max_tokens=256,
)

result = await verifier.verify(
    window=normalized_intents,           # list[NormalizedIntent]
    task_signal_hint="focused_mutative", # optional: helps Claude interpret context
    policy_name="focused_mutative",      # optional: helps Claude interpret context
)

print(result.score)    # float 0.0 – 1.0
print(result.cls)      # AnomalyClass.NORMAL | SUSPICIOUS | CRITICAL
print(result.reasons)  # ("secret_access_after_external_read", ...)

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Integration with MLAnomalyDetector

The typical integration is as a gray_zone_verifier in MLAnomalyDetector:

import anthropic
from axor_classifier_simple import MLAnomalyDetector
from axor_classifier_llm import LLMAnomalyVerifier

verifier = LLMAnomalyVerifier(client=anthropic.AsyncAnthropic())

detector = MLAnomalyDetector(
    gray_zone_verifier=verifier,
    gray_zone_threshold=0.50,   # escalate when score >= 0.50 in suspicious range
)

result = await detector.score(window=intents)

Escalation logic in MLAnomalyDetector:

Score	Class	LLM called?
< 0.40	NORMAL	No
[0.40, 0.50)	SUSPICIOUS	No (below threshold)
[0.50, 0.75)	SUSPICIOUS	Yes — verifier result returned
>= 0.75	CRITICAL	No

If the LLM call raises any exception, MLAnomalyDetector falls back to the ML-derived score and logs a warning.

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What the LLM sees

Each NormalizedIntent in the window is formatted as a single line:

tool=bash op=execute_generated_code target=workdir dest=none prov=repo flow=local_to_local [executes_generated_code, after_external_read]

The flags in brackets (reads_secret, writes_outside_workdir, etc.) appear only when set.

The system prompt instructs Claude to evaluate behavioral patterns only:

• External read → secret access → outbound network → critical
• Cloud metadata probe → critical
• Docker socket access → critical
• Unexpected tool class for stated task → suspicious
• Normal coding / research patterns → normal

Claude responds with JSON only:

{
  "score": 0.82,
  "class": "critical",
  "reasons": ["secret_access_after_external_read", "network_after_secret"]
}

Malformed responses are caught: _parse_response returns score=0.5 / SUSPICIOUS / ("verifier_parse_error",) and logs the exception at WARNING level.

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Parameters

Parameter	Default	Description
client	required	anthropic.AsyncAnthropic instance
model	claude-haiku-4-5-20251001	Anthropic model ID
max_tokens	256	Max tokens for the verifier response

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AnomalyResult contract

Defined in axor_core.contracts.anomaly:

@dataclass
class AnomalyResult:
    score: float              # 0.0 – 1.0 risk score
    cls: AnomalyClass         # NORMAL | SUSPICIOUS | CRITICAL
    reasons: tuple[str, ...]  # human-readable trigger reasons

Score thresholds:

Class	Range
NORMAL	[0.0, 0.40)
SUSPICIOUS	[0.40, 0.75)
CRITICAL	[0.75, 1.0]

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Development

git clone https://github.com/Bucha11/axor-classifier-llm
cd axor-classifier-llm
pip install -e ".[dev]"
pytest tests/

Tests mock the Anthropic client and do not make real API calls.

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License

MIT