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Combining five useful anomaly signals gave one self-hosted LLM guard a 42.9% false-positive rate on clean production text. The author, Farid Aghayev, reports that changing the fusion rule—and then tuning individual detectors—reduced the measured rate to 0.024% and eventually 0.000% in his system. Those are results from one case study, not general performance estimates for LLM safeguards.
What this guard detects—and what it does not
Aghayev uses “decoding corruption” operationally for a generation stream that stops answering and veers into unrelated or malformed material. That is different from a hallucination in the ordinary sense: a hallucinated answer can remain coherent while making false claims, whereas this guard looks for visible derailment in the stream.
It is an anomaly guard, not a truth checker. Fluent output that remains on-topic, in the expected script, and statistically ordinary can evade it. The author says subtle factual errors are handled separately with grounding or factuality methods.
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The described implementation has one state object ingest characters incrementally and maintain shared statistics. At checkpoints, detectors read that state rather than each making a separate pass over the output.
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| Signal | What it watches |
|---|---|
| Deterministic rules | Measures including digit fraction, symbol density, and the longest run of identical characters. |
| Character n-gram surprise | Unexpected character sequences, using a model trained on the stream’s clean prefix. |
| Count-Min Sketch repetition | Repeated character shingles, summarized with a Count-Min Sketch. |
| Rolling SimHash | Similarity to a fingerprint captured at the end of the clean prefix. |
| Character entropy | Statistical patterns intended to react to table dumps and degenerate loops. |
Aghayev describes the implementation as bounded-memory and reports processing about 50,000 characters per second on one CPU core. That is a figure for his implementation, not a general benchmark.
Why noisy-OR produced so many false alarms
The initial fusion combined detector scores with noisy-OR: each signal’s chance of flagging contributed to the overall score. That approach can be useful when signals are independent, but mild signals can accumulate even when no one detector sees a convincing anomaly.
In the author’s production text, legitimate economic content included many figures, percentages, variable codes, and markdown tables. Several detectors could therefore register weak signals on healthy output. Aghayev reports that the noisy-OR configuration falsely flagged 42.9% of clean production text—high enough to make it unusable in the described workflow.
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The next design let the strongest individual detector dominate, with a limited bonus when at least two detectors crossed a threshold. Aghayev reports a 0.024% false-positive rate after that change. Further detector-specific adjustments reportedly brought the measured rate to 0.000%:
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- Limit SimHash’s contribution.
- Gate the surprise model behind repetition.
- Score sketch repetition by rate rather than maximum count.
These numbers describe the author’s system and evaluation; they have not been established as independently replicated results. The case study’s practical lesson is that combining signals is itself a consequential design choice. More detectors do not necessarily produce a safer guard if weak, correlated evidence can pile up.
What the evaluation supports
Aghayev reports replaying the finished guard over 121 historical messages: it flagged two, which he identified as actual prior corruptions, and did not flag the other 119. The reported benchmark also used synthetic positives, created by injecting four corruption classes into clean text at controlled onsets. The author says those examples matched known historical failures, while noting that future production traffic may contain patterns the test set did not model.
This is useful evidence about a particular system, but it is not a broad estimate of real-world detection accuracy. The historical set is limited, and synthetic examples cannot establish how well the guard handles every unseen failure mode. In particular, the evaluation does not resolve the stated limitation: statistically ordinary, fluent, on-topic corruption may remain undetected.
Streaming behavior, calibration, and cancellation
Buffering and checkpoints
The described protocol holds the first 350 characters and forwards none until that prefix has been inspected. If the verdict is clean, the system flushes the buffer, freezes its self-calibrated baselines, and checks each additional 400 characters. It requires two consecutive qualifying checks before aborting. Aghayev estimates that the initial hold adds about one second at typical decoding speeds.
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Choosing thresholds
The author describes split-conformal calibration with held-out clean streams and an operator-selected false-alarm budget. The finite-sample guarantee depends on exchangeability: the calibration examples and future clean examples must be drawn under sufficiently comparable conditions. A distribution shift, or production corruption unlike the modeled examples, is not covered by that guarantee.
Handling an abort in the transport
The guard raises abort as an exception inside the token sink. In the author’s integration, the transport initially interpreted that cancellation as an upstream failure, creating a risk that it would trigger the HTTP circuit breaker. The implementation lesson is to distinguish caller-side cancellation from an upstream service failure in the transport’s error handling.
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A multi-signal streaming guard is most relevant when a self-hosted model sometimes produces conspicuous stream derailments and the application needs to stop forwarding them. Before deployment, evaluate it against representative clean traffic from the actual domain: text rich in numbers, codes, tables, or unusual formatting can trigger mild signals that look suspicious in a generic baseline.
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- Measure false alarms on clean, domain-specific streams—not just synthetic or generic text.
- Test known corruption patterns at different points in a stream, while treating synthetic positives as limited evidence.
- Choose and validate the fusion rule; do not assume that adding signals automatically improves reliability.
- Set expectations clearly: this kind of guard targets visible anomalies, not factual correctness.
- Test buffering, latency, cancellation, and circuit-breaker behavior in the full streaming path.
Aghayev’s case study centers on a concrete engineering warning: when text styles vary, the combination rule can matter more than the detector roster.
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