finding-discovery

द्वारा openai

उपयोग करें जब Codex पहले से ही सुरक्षा स्कैन के finding-discovery चरण में हो या उपयोगकर्ता स्पष्ट रूप से रिपॉजिटरी में संभावित सुरक्षा निष्कर्षों की खोज करने के लिए कहता है…

npx skills add https://github.com/openai/codex-security --skill finding-discovery

Security Finding Discovery

Identify technically plausible security vulnerabilities in the authorized source, using the threat model and applicable security policy. Discovery preserves evidence for later validation; it does not decide final severity.

Read ../../references/artifact-storage.md before saving output and ../../references/security-guidance.md before source review. Resolve applicable policy for each reviewed path, including delegated assignments. Honor explicit input and output paths; ask for an explicitly required input only when its absence prevents the requested analysis.

Select the Workflow

Standard and Deep workers use their coordinator's self-contained prompts rather than this phase skill.

  • Running compact diff: read the supplied inventory through list_codex_security_review_items, review every assigned changed file, and record all candidates once with record_codex_security_discovery_candidates. Do not create ranked worklists, per-finding ledgers, receipts, or discovery reports.
  • Standalone diff without a compact inventory: use ../security-scan/references/scan-artifacts-and-ledger.md for the changed-file worklist and retained phase artifacts.
  • Standalone repository discovery: apply the evidence criteria below to the authorized source and return the candidates and coverage limitations. Do not start a scan or run the diff worklist pipeline.

For a diff, inspect the actual changed behavior, including deleted files at the baseline revision. Follow supporting code far enough to understand the affected boundary; expand to sibling callers when a changed shared control affects them. Do not broaden into an unrelated repository audit.

Evidence Criteria

  • Trace attacker-controlled input or a privileged operation through the closest relevant control to the sink and security impact. Explain why the control is absent, bypassable, or applied to the wrong value or boundary.
  • Check runtime reachability and deployment assumptions. Record effective guards and containment as counterevidence; a safe sibling implementation does not establish that the candidate path is safe.
  • Preserve the root-control location as well as entrypoints, wrappers, concrete implementations, and sinks needed to understand the claim. When the claim covers several implementations, inspect those implementations rather than asserting that all are affected.
  • Keep independently reachable vulnerable instances addressable. Merge duplicate observations of the same source/control/sink/impact instead of splitting cosmetic variants; retain all affected locations and original candidate IDs when merging.
  • Follow explicit user or advisory seeds to their exact source and control. Record the evidence that supports, disproves, or leaves the seed unresolved; a nearby issue in the same family does not close it. Use only authorized source material and do not fetch advisory links without permission.
  • Distinguish concrete security impact from maintainability or robustness concerns. A crash, dangerous API, optional feature, or missing check is a lead, not a finding by itself. Carry plausible candidates with explicit proof gaps to validation rather than inventing evidence or discarding them for lack of a runnable reproduction.

Divide independent review assignments among available workers when useful, with the target, scope, relevant policy/model, and required result fields. The parent reviews unassigned work and reconciles duplicates. Do not repeat validation or attack-path phases inside discovery.

Candidate Output

For each candidate, retain:

  • Stable candidate ID and title; preserve supplied seed or ledger IDs and advisory references.
  • Affected file/line locations, labeling entrypoint/wrapper, root_control, sink, and concrete_implementation where useful.
  • Attacker-controlled source, closest control, sink or broken control, plausible impact, and the source evidence connecting them.
  • Counterevidence, deployment preconditions, unresolved questions, and what validation needs to establish.
  • CWE IDs when known, otherwise an empty list.
  • For repository or scoped-path instances, an instance key <family>:<file>:<line> when required by the caller.
  • For diff findings, relevant_lines only where relevant buggy behavior overlaps the diff.

Use the caller's structured candidate contract. For a legacy diff, save the discovery report and discovery receipts at the paths in ../../references/scan-artifacts.md; subsequent phases own their own evidence and receipts. Finish when assigned source review and concrete follow-ups are accounted for, recording unfinished review honestly. If no plausible candidates remain, return that result with the actual reviewed scope and limitations.

openai की और Skills

release
openai
Symphony रिलीज़ को बंप करके, उसे लैंड करके, मर्ज किए गए कमिट को टैग करके, और Burrito रिलीज़ वर्कफ़्लो को सत्यापित करके काटें। जब ऐसा करने के लिए कहा जाए…
signing-entitlements
openai
macOS ऐप्स के लिए हस्ताक्षर, अधिकार, कठोर रनटाइम और गेटकीपर समस्याओं का निरीक्षण करें। जब कोड हस्ताक्षर विफलताओं, लापता अधिकारों,… के निदान के लिए कहा जाए तो उपयोग करें।
building-ai-agent-on-cloudflare
openai
Cloudflare पर Agents SDK का उपयोग करके AI एजेंट बनाता है, जिसमें state प्रबंधन, real-time WebSockets, अनुसूचित कार्य, tool एकीकरण और चैट शामिल हैं…
epigraphdb-skill
openai
ऑन्टोलॉजी, साहित्य, एमआर, जीन-दवा और समर्थन-पथ साक्ष्य के लिए कॉम्पैक्ट EpiGraphDB API अनुरोध सबमिट करें। जब उपयोगकर्ता संक्षिप्त EpiGraphDB सारांश चाहता है तब उपयोग करें।
runtime-behavior-probe
openai
अस्थायी जांच स्क्रिप्ट, सत्यापन मैट्रिक्स, स्थिति नियंत्रण और निष्कर्ष-प्रथम रिपोर्ट के साथ runtime-behavior जांच की योजना बनाएं और निष्पादित करें। केवल तब उपयोग करें जब...
deep-security-scan
openai
उपयोग तब करें जब उपयोगकर्ता गहन, व्यापक, बहु-पास, या विचरण-न्यूनीकरण वाले रिपॉजिटरी-व्यापी या स्कोप्ड-पथ Codex Security स्कैन का अनुरोध करता है। कई स्वतंत्र बार-बार चलाएँ…
define-security-policy
openai
किसी रिपॉज़िटरी या कंपोनेंट के लिए SECURITY.md मार्गदर्शन को परिभाषित, समीक्षा, या अपडेट करें। उपयोग करें जब उपयोगकर्ता यह स्पष्ट करना चाहता है कि Codex Security को क्या समीक्षा करनी चाहिए, क्या बाहर…
validation
openai
इसका उपयोग तब करें जब Codex पहले से ही सुरक्षा स्कैन के सत्यापन चरण में हो या उपयोगकर्ता स्पष्ट रूप से यह निर्धारित करने के लिए कहे कि एक या अधिक उम्मीदवार सुरक्षा निष्कर्ष…