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The setup
The author used a bitsandbytes (bnb) quantized Qwen3-8B model and a custom dataset of fewer than 250 examples. The task was a structured analysis he calls the Memory Security Model (MSM), which asks a model to assess how an AI agent’s memory could be attacked or misused.
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The training set mixed three kinds of input: attack scenarios, legitimate benign scenarios, and out-of-scope prompts that have nothing to do with security. Each target response filled in the same eight fields:
- components
- trust boundary
- memory type
- threat classification
- lifecycle depth
- invariant check
- severity
- recommended response
Training ran for three epochs. The author then scored 39 held-out scenarios field by field, comparing the base model and the fine-tuned model against ground-truth labels. The evaluation covers one training run, one dataset, and one schema, so the results describe that setup only.
#1 Best Overall
Scores by field
These are the author’s reported figures from this run. Each field has its own denominator, because some labels were not applicable to every scenario or some records were incomplete.
| Field | Base model | Fine-tuned model | Change |
|---|---|---|---|
| Threat classification | 13/33 (39.4%) | 15/33 (45.5%) | +6.1 percentage points |
| Lifecycle depth | 20/29 (69.0%) | 16/29 (55.2%) | −13.8 percentage points |
| Severity | 14/30 (46.7%) | 11/30 (36.7%) | −10.0 percentage points |
| In-scope determination | 32/33 (97.0%) | 32/33 (97.0%) | No change |
The six highlighted cases discussed below were selected examples. They are not a random sample, and they are not the full table.
Where the numbers moved
Read the raw counts rather than the percentages. The percentages make the changes look larger than they are on a set this small.
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Threat classification: two more correct answers
Threat classification rose from 13 to 15 correct out of 33. That is a real improvement in the direction the training data pointed, but it amounts to two scenarios. It is the only field in the table that moved up.
Lifecycle depth: four fewer correct answers
Lifecycle depth fell from 20 to 16 correct out of 29. This field asks the model to judge how far through the memory lifecycle an attack reaches, which is a graded judgment rather than a label choice. The author’s reading is that fine-tuning on a small set of examples did not preserve this kind of judgment.
Severity: three fewer correct answers
Severity fell from 14 to 11 correct out of 30. Like lifecycle depth, severity requires weighing context rather than picking a category, and it also declined.
Rank #3
In-scope determination: unchanged, but the errors differ
Both models classified 32 of 33 in-scope items correctly, so the aggregate showed no change. The author points out that the single error each model made was different in kind. A matching score therefore says nothing about whether the models fail in the same way, and the failures themselves are what matter.
Three failures the averages did not show
The six highlighted cases make the mechanism concrete. Each one shows an output that looked acceptable on the surface.
A correctly formatted verdict that was wrong
In an attack scenario involving targeted deletion, the base model correctly identified the request as malicious. The tuned model filled in every field in the requested schema and labeled the same request benign. Schema compliance and a confident tone did not reveal the error. Only a check against the verdict itself did.
A repetition loop
In a benign scenario, the tuned model’s output started correctly and then repeated near-identical invariant-check phrases until generation stopped. Field-level scoring can miss this kind of failure, because the early fields may still be right. Termination behavior has to be checked directly.
An out-of-scope prompt treated as a security case
Given a weather query, the tuned model invented a security-analysis framing and returned an “Allow” verdict. The correct behavior was to decline an unrelated request. The author’s examples are documented individual cases, and he does not claim to know how often such failures occur beyond the cases he tested.
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How to evaluate a fine-tune like this
The case suggests a sequence for anyone checking a similar small-model fine-tune on a structured judgment task:
Best Value
- Score each output field separately, using the applicable denominator for each. Keep categorical fields (such as threat type) apart from graded or compositional fields (such as severity and lifecycle depth), because they can move in opposite directions.
- Include explicit out-of-scope prompts and record whether the model declines them. Then read what a wrong answer does, not only whether the overall count changed.
- Review semantic correctness separately from format. Check that the verdict is right, not just that the output matches the schema.
- Check termination. Look for repeated phrases and outputs that run until the token limit.
- Do not rely on loss curves or one aggregate score to establish reliability. The author’s position is that a field-level breakdown, plus a specific check on whether the most confident-looking, best-formatted outputs are also the most accurate, is necessary in a way a single top-line number cannot replace.
What this case does and does not establish
This is a small, single-run, author-reported case study. It does not show that every small-model fine-tune behaves this way. Nor does it provide an independent replication, a public evaluation set that outside reviewers have audited, or a benchmark that others can rerun. The author’s discussion of prior literature is his own framing of that work and is not independently verified here.
What the case does show is narrower and still useful: a training run can improve one judgment and degrade others on the same task, and a model can look correct on the one number most people check while failing on the fields and inputs that matter most in a security setting.
The author says his next step is automated red-teaming and evaluation, and he names the Garak framework as a way to make probing repeatable at larger scale. That is a stated direction for his work, not a result reported in this case.
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