EVIDENCE AND EVALUATION / Undominated.ai
Undominated · Adversarial suffix detection audit
Checks a supplied perplexity-ratio assertion and anomaly threshold over local numeric input; a flag is a heuristic signal for review, not proof of an attack.
“Check a perplexity-ratio assertion and a supplied anomaly threshold over local numeric input. A threshold flag is a heuristic signal for review, not proof of a GCG suffix, prompt injection, or an actual attack.”
01 / THE REASONING
Why this made the selection.
- Pairs a bounded numeric audit with an offline Python checker and an explicitly synthetic threshold-breach example.
- Recomputes the ratio from supplied inputs instead of trusting the asserted anomaly ratio.
- Distinguishes input errors, ratio-assertion mismatches and threshold breaches without claiming attack attribution.
02 / THE REVIEW RECORD
What we actually inspected.
Source review has boundaries.
A clear record is more useful than a “safe” badge.
Material inspected
- skills/undominated-adversarial-suffix-detection-audit/SKILL.md
- skills/undominated-adversarial-suffix-detection-audit/scripts/check.py
- skills/undominated-adversarial-suffix-detection-audit/examples/synthetic.json
Our findings
- First-party resource authored by Undominated.ai; this is our own verification record, not an independent endorsement.
- The download preserves the complete source and supporting files. Its manifest hashes identify the exact bytes.
- The checker requires prompt_perplexity, baseline_normal_perplexity, anomaly_ratio and max_allowed_anomaly_ratio; the baseline must be positive and all inputs finite, non-negative numbers or decimal strings.
- Exact fraction arithmetic recomputes the ratio and compares its unrounded value to the threshold. The supplied ratio must match two-decimal half-up rounding.
- Eight local regression tests passed for benign input, threshold equality and a small breach, half-up rounding, forged and old ratio assertions, invalid or missing values, and CLI exit codes 0/1/2. The synthetic 245.8 / 32.4 example reports 7.59 and exits 1 with FLAGGED.
- The compatibility field attack_detected records a numeric threshold breach only; no model execution or actual attack detection was tested.
Not established by this review
- Perplexity measurement or provenance of supplied observations
- Accuracy against real GCG suffixes, prompt injections or other attacks
- Suitability of a chosen anomaly threshold
- Model compliance with these instructions on arbitrary tasks
- Native integration in every agent host
- Public GitHub source identity
- Remote installation through the standard Skills CLI
- The new npm resource CLI installation
The review applies to the material and revision named here. A newer upstream release can change its behavior.
03 / PUT IT TO WORK
Add a skill to your workflow.
- Download the complete bundle from https://undominated.ai/resources/skills/undominated-adversarial-suffix-detection-audit/bundle.zip and extract it into a new directory.
- Place the extracted directory at .agents/skills/undominated-adversarial-suffix-detection-audit/ or your host's documented skills directory. Preserve SKILL.md, scripts, examples and licence files together.
- Run the documented synthetic example from the skill directory with Python 3.10 or later before using your own evidence.
Before you start
- An Agent Skills compatible host
- Python 3.10 or later for the optional checker
THE COMPLETE REVIEWED DEFINITION
Read it before you reuse it.
Original source bytes, with attribution.
Review the host-specific setup notes above.
# Adversarial suffix detection audit
## Description
Check a perplexity-ratio assertion and a supplied anomaly threshold over local numeric input. A threshold flag is a heuristic signal for review, not proof of a GCG suffix, prompt injection, or an actual attack.
## Category
`evaluation`
## Tags
`evaluation`, `security`, `jailbreak`, `gcg`, `perplexity`
## When to Use
Review a proposed perplexity threshold against supplied observations before deciding what further model or security evaluation is needed.
## Operational Limitations
The checker does not measure perplexity, inspect prompts, execute a target model, or establish detector accuracy. It recomputes a ratio from the supplied numbers; their provenance and a threshold's suitability remain unverified. The compatibility field `attack_detected` means only that the unrounded ratio exceeded the supplied threshold. It is not attack attribution or a security verdict.
## Input Contract
Supply a JSON object containing these four numeric fields (JSON numbers or decimal strings):
- `prompt_perplexity`: finite and non-negative.
- `baseline_normal_perplexity`: finite and strictly positive.
- `anomaly_ratio`: the asserted quotient `prompt_perplexity / baseline_normal_perplexity`, rounded to two decimal places using half-up rounding.
- `max_allowed_anomaly_ratio`: a finite, non-negative threshold.
Boolean, missing, nonnumeric, nonfinite and negative values are invalid. The reported ratio and threshold must fit finite JSON numbers. Threshold comparison uses the exact, unrounded quotient: equality passes, and any value above the threshold flags even if the displayed two-decimal ratio equals the threshold. A mismatched `anomaly_ratio` assertion also requires review; it cannot hide a threshold breach.
## Verification Script
Run the deterministic verification checker:
```bash
python3 scripts/check.py examples/synthetic.json
```
The supplied example intentionally exceeds its threshold and returns **1** with `status: FLAGGED`. Its quotient is `245.8 / 32.4`, whose two-decimal half-up assertion is `7.59`.
Exit codes:
- `0`: Valid input, a matching ratio assertion and a ratio at or below the threshold.
- `1`: Threshold breach or ratio-assertion mismatch; review the structured `issues`.
- `2`: Schema error, unreadable input or invalid usage; see the structured error.
The download contains SKILL.md. Extract the whole bundle; the supporting files are required. Inspect the included MANIFEST.json for file hashes.
By Undominated.ai. Exact upstream source ↗ · Licence · Attribution
SHA-256 0a1d5d8143f5146aace29234f376214d22d518de1d6e911b7c78a92d55c417fa
Read the applicable licence
MIT License Copyright (c) 2026 Undominated.ai Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
04 / FOLLOW THE EVIDENCE
The source trail.
Our notes are separate from the original resource.
Check upstream before adopting a new version.
https://undominated.ai/resources/skills/undominated-adversarial-suffix-detection-audit/definition.md
Supports: Workflow and input requirements, Stated limitations
- MIT licence ↗Checked
https://undominated.ai/resources/skills/undominated-adversarial-suffix-detection-audit/LICENSE.txt
Supports: Redistribution terms and attribution
- Complete source bundle ↗Checked
https://undominated.ai/resources/skills/undominated-adversarial-suffix-detection-audit/bundle.zip
Supports: Full local source, supporting files and integrity manifest