Is Shuorenhua safe?
- Python shell/command execution
- Python dynamic code execution
- Python network egress
repo is an AI agent_skill analyzed by SkillTotal's deterministic static scanner. The scan found no malicious indicators, though 5 risky constructs are reported for review. It can: dynamic code execution, filesystem read, filesystem write, network egress and shell execution — capabilities are what the code can do, not a verdict on intent. Risk score 0/100 (low).
repo
Automated static-analysis result. It can contain false positives and false negatives, and is not a claim about the intent of Shuorenhua's authors. Report a false positive.
Behavioral traits
How this component maps to the CSA agentic threat model. Descriptive — it never affects the risk score.
Findings (5)
The code turns strings into live code at runtime (eval / new Function / exec).
exec(compile(source, str(path), "exec"), module.__dict__)
Why it matters: If those strings aren't fixed and trusted, they become a way to run arbitrary code.
Fix: Avoid evaluating dynamically constructed code; if unavoidable, ensure the input is a trusted constant and never derived from external data.
The component can run operating-system commands or spawn processes.
result = subprocess.run(
[sys.executable, str(script), "--check"],
cwd=ROOT,
capture_output=True,
text=True,
check=False,
)out = subprocess.run(
["gh", "api", "graphql", "-f", f"query={query}"],
stdout=subprocess.PIPE, text=True, check=True).stdoutWhy it matters: Powerful and often legitimate — confirm the commands aren't built from untrusted input.
Fix: Confirm the command and its arguments are fully controlled and not derived from untrusted input; avoid shell=True.
The component reads files from disk.
return (ROOT / relative).read_text(encoding="utf-8")
benchmark = read_text("evals/benchmark.md", issues, "counts")samples = read_text("evals/real-samples.md", issues, "counts")text = read_text(relative, issues, "counts")
files.append((relative_text, path.read_text(encoding="utf-8")))
skill = read_text("SKILL.md", issues, "meta")text = read_text(relative, issues, "meta")
source = path.read_text(encoding="utf-8")
text = read_text("references/structures.md", issues, "rule-tables")text = path.read_text(encoding="utf-8")
blocks = extract_blocks(path.read_text(encoding="utf-8"))
results.append(analyze_case(cid, scene, quote, output_text, path.read_text(encoding="utf-8")))
return Path(path).read_text(encoding="utf-8")
cases = parse_cases(src.read_text(encoding="utf-8"))
if not path.is_file() or path.read_bytes() != expected.encode("utf-8")Why it matters: Usually legitimate, but worth confirming it can't be steered into reading sensitive files.
Fix: Confirm which files are read and that paths cannot be influenced by untrusted input to reach sensitive locations.
The component writes or deletes files on disk.
md_path.write_text("\n".join(header).rstrip() + "\n", encoding="utf-8")json_path.write_text(
json.dumps({"reports": model_reports, "cases": file_results}, ensure_ascii=False, indent=2) + "\n",
encoding="utf-8",
)path.write_text(text, encoding="utf-8")
out.write_text(svg, encoding="utf-8")
Why it matters: Usually legitimate, but worth confirming the paths can't be controlled by untrusted input.
Fix: Confirm which files are written/deleted and that paths cannot be influenced by untrusted input.
The component makes outbound network requests.
from urllib.parse import unquote
return unquote(target.split("#", 1)[0].split("?", 1)[0]) or NoneWhy it matters: Usually legitimate, but confirm the destinations are expected and no sensitive data leaves.
Fix: Confirm the destination hosts are expected and that no sensitive data is sent off-host.
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