record_type string | vuln_class string | count int64 | total int64 | polyglot int64 | classes int64 | languages int64 |
|---|---|---|---|---|---|---|
class_index | cmdi | 6 | null | null | null | null |
class_index | nosql-ldap | 3 | null | null | null | null |
class_index | prompt-injection | 4 | null | null | null | null |
class_index | redirect | 3 | null | null | null | null |
class_index | sqli | 8 | null | null | null | null |
class_index | ssrf | 5 | null | null | null | null |
class_index | traversal | 5 | null | null | null | null |
class_index | xss | 9 | null | null | null | null |
totals | null | null | 43 | 24 | 8 | 12 |
ποΈ Polyglot Bug Patterns
π Dataset Β· π¨ Space Β· π§ Model Β· π» GitHub
Bro, imagine a dataset of 43 non-destructive attack payloads across 8 bug classes β 24 of them polyglot, meaning a single string that's simultaneously plausible HTML, JS, SQL and shell, so it escapes whatever context your app dropped it into. Plus real detector output from a full multimodal scan.
Everything here is synthesised or locally generated. No real-world traffic, no scraped data, no user data.
π¦ What's inside
| Config | Rows | What it is |
|---|---|---|
payloads |
43 | Every detection payload: value, bug class, CWE, OWASP, CVSS v3.1 vector+score, polyglot flag, and a destructive safety flag. |
findings |
36 | Real findings from one full scan of the bundled demo target, with proof strings, endpoints, remediation, tags. |
scans |
1 | Scan-level rollup: pages scanned, risk score, counts by severity, modality breakdown. |
class_index |
9 | Per-class counts plus totals. |
Each config ships JSONL and Parquet, so both of these work:
from datasets import load_dataset
ds = load_dataset("Kicaulah/polyglot-bug-patterns", "payloads")
print(ds["train"][0]["value"])
print(ds["train"].filter(lambda r: r["polyglot"])[0]["value"])
findings = load_dataset("Kicaulah/polyglot-bug-patterns", "findings")
print(len(findings["train"]), "findings from one scan")
𧬠The payload classes
| Class | n | Examples |
|---|---|---|
sqli |
8 | ' OR 1=1 -- , UNION SELECT NULL, MySQL error-based double-query, ORDER BY 99 column-count probe |
xss |
9 | '"><svg/onload=alert(1)> attr breakout, </script><script> closer, JS-string escape, {{7*7}} SSTI |
cmdi |
6 | ;MARKER;, $(echo β¦), backticks, %0a CRLF, ${IFS} |
traversal |
5 | ..%2f..%2f..%2fetc%2fpasswd, ....//....//, double-double-encoded |
ssrf |
5 | http://MARKER.oast.invalid/, 127.0.0.1, file:///, gopher://, [::1] |
prompt-injection |
4 | "Ignore all previous instructions", </s>[INST] β¦ [/INST] chat-template escape, markdown-fence system override |
nosql-ldap |
3 | *(), '; return true; //, LDAP wildcard filter |
redirect |
3 | absolute, protocol-relative, backslash open redirect |
Every payload carries a unique marker (PBHX7) so reflection is unambiguous β
that's what makes these usable as training signal for a detector or as a fixture
in your own test suite.
β οΈ Safety properties
This is a defensive dataset. Concretely:
- Every payload is non-destructive by construction. The
destructivecolumn isfalsefor all 43 rows β verified by the samesafety.is_forbidden_payload()gate the scanner itself uses at runtime. - No
DROP/DELETE/UPDATE/INSERT, nosystem()/exec()/xp_cmdshell, nosleep()/benchmark(), no reverse shells, no persistence. - Time-based blind SQLi is deliberately absent β
sleep()payloads are blocked project-wide. - Payloads are for systems you own or are authorized to test. Using them against someone else's production box is illegal in most jurisdictions.
π§ͺ Using it
As a test fixture β assert your WAF/filter catches the polyglot set:
from datasets import load_dataset
ds = load_dataset("Kicaulah/polyglot-bug-patterns", "payloads")["train"]
for row in ds.filter(lambda r: r["vuln_class"] == "xss"):
print(row["value"]) # feed each through your own escaping function
As detector training data β findings pairs a payload with the signal that
proved it (proof), the CVSS vector, and the remediation. That's a supervised
signal for "was this injection real or a reflection".
As an LLM security eval β prompt-injection rows are ready-made instruction
overrides for testing whether your model or agent guardrails hold.
π Provenance
- payloads β hand-authored from public documentation (OWASP WSTG, PortSwigger cheat sheets, public CVE writeups), rewritten to be non-destructive and marker-tagged.
- findings / scans β actual output of
Hunter.demo()againstpolyglot_bug_hunter.demo_target, an intentionally vulnerable app that runs on localhost inside the test process. Regenerate withpython tools/build_dataset.py.
Nothing was scraped from production systems.
π Changelog
- v1.0.0 β initial release: 43 payloads / 8 classes (24 polyglot), 36 findings from one full 4-modality demo scan, JSONL + Parquet, 12 languages declared.
π€ Contribute a pattern
Missing a class? Open an issue or PR. The bar: it must be non-destructive (the automated gate enforces it), documented with a CWE and an OWASP mapping, and tagged for what detector signal it should produce.
MIT Β· π·οΈ PolyglotBugHunter-X Β· authorized security testing only
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