adaptyv
2 findings (2 high), mainly data exfiltration. Most recent scan 2026-10-21.
Second scanner: NVIDIA SkillSpector
Independent static run, no language model, over the same files as the Cisco scan above. How the two scanners compare across the whole corpus: Two scanners, one corpus.
What this skill does
Uses the Adaptyv Bio Foundry API and Python SDK to design protein characterization experiments, estimate costs, submit sequences, monitor laboratory progress, and retrieve results. Applies to Adaptyv Foundry, its target catalog, binding screening and affinity assays, thermostability, expression, fluorescence, epitope binning, and enzyme activity workflows, including code using adaptyv or FoundryClient.
- Package
- 2 files, 33 KB
- SKILL.md
- 1577 words, 12+ sections
- Code
- None. Instructions only.
- License
- MIT
- Compatibility
- Requires network access, an Adaptyv Foundry account and bearer token. Python examples require Python 3.11+ and adaptyv-sdk installed from its official GitHub repository; direct REST examples use httpx.
SKILL.md covers: Adaptyv Bio Foundry API · Connect · ADAPTYV_API_KEY is already set in the environment. · Python SDK · Browse, estimate, create a draft · Review vendor, catalog number, construct, and pricing before selecting an ID. · candidates.json is a map of unique names to full amino acid sequences. · SDK takes the spec itself; REST takes {"experiment_spec": spec}. · After the batch and estimated cost have been reviewed: · Submit, then inspect and confirm the quote · Quote generation is asynchronous. Retry get_quote with a bounded timeout if it · returns 404 after successful submission; a draft has no forthcoming quote.
Findings
HIGH Sensitive source reaches a network sink Data Exfiltrationcorrelation
Correlated sensitive_environment → network behavior across SKILL.md.
SKILL.md:151
HIGH Sensitive source reaches a network sink Data Exfiltrationcorrelation
Correlated sensitive_environment → network behavior across SKILL.md.
SKILL.md:158
What is in the package
| File | Kind | Size |
|---|---|---|
SKILL.md | doc | 13 KB |
references/api-endpoints.md | doc | 20 KB |
External hosts referenced
devs.adaptyvbio.com arxiv.org github.com docs.adaptyvbio.com doi.org export.arxiv.org foundry.adaptyvbio.com
Hostnames that appear in the skill's files. Referencing a host is not a finding; compare against the findings above and the skill's stated purpose.
Environment variables referenced
ADAPTYV_API_KEY ADAPTYV_API_URL FOUNDRY_API_TOKEN
Package contents recorded 2026-10-22 at commit 593377bb0487. The scan above ran against the version current at scan time; the two can differ if the skill has since been updated.
What we checked
- Static analyzer. Pattern rules over SKILL.md and bundled files: known-bad commands, exfiltration URLs, secrets, obfuscation.
- Bytecode analyzer.
- Pipeline analyzer.
- Correlation analyzer.
- Behavioral analyzer. Checks what the skill asks the agent to do against what its description claims it does.
- Trigger analyzer. Looks for instructions that fire on conditions the user did not ask for.
What we did not check
- Runtime behaviour. The skill was not executed; findings come from reading its files.
- Anything fetched at install or run time, including remote scripts, package dependencies, and URLs the skill tells the agent to open.
- Content the analyzers cannot read: images, binaries, and some non-English text.
- Known scanner bypass classes. See the methodology and limitations page.
Sources
- Repository: https://github.com/K-Dense-AI/scientific-agent-skills
- Raw scan result: skills-adaptyv-scan.json
- Badge for your README:
- Request a re-scan