菜单
Detects inference endpoints without authentication or throttling, allowing model weight reconstruction. Use when writing inference API endpoints, deploying LLM-serving infrastructure, implementing model access controls, or configuring rate limiting and authentication for model endpoints.
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基于 SOC 职业分类
Detects supply-chain risks in dependency manifests, lockfiles, install commands, and CI pipelines — missing lockfiles, absent vulnerability scanners, unverified installs, install hooks that pipe remote content to a shell, and AI-hallucinated package names. Use when writing package install commands, dependency manifests, CI/CD configs, build scripts, or code that loads external packages.
Deep review that audits API contracts for mismatches between what callers assume and what implementations enforce. Designed for nightly CI, pre-release scans, or manual deep audits — runs in tens of minutes, not seconds. Surfaces bugs that single-pass OWASP review misses — caller/callee invariant gaps, trust-anchor confusion, predicate misnaming.
Runs a full OWASP/CWE security audit via isolated subagents. Use when the user types /security-review or explicitly requests a full security audit of the current code.
Detects weak password storage, flawed JWT validation, sessions surviving logout, and missing MFA in authentication code. Use when writing login flows, session management, password storage, JWT handling, MFA implementation, credential validation, or any code that authenticates users. Also invoke when implementing "remember me" functionality or API key management.
Detects missing ownership checks, broken role enforcement, and IDOR vulnerabilities that let users access other users' resources. Use when writing code that checks authorization, enforces resource ownership, handles IDOR (object-level authorization), processes server-side requests to external URLs, or implements access control middleware.
Detects multi-threaded code where a lock is held across a blocking operation, lock acquisition order risks deadlock, lock-free atomics use the wrong memory ordering, or double-checked locking is missing the necessary barrier. Use when writing or modifying code that acquires multiple locks, uses atomic operations with explicit memory orders (std::memory_order_*, atomic.LoadAcquire, Ordering::Relaxed), implements lazy initialization with double-checked locking, or calls a blocking operation (I/O, sleep, await, blocking channel send) inside a locked region.
| name | model-theft |
| description | Detects inference endpoints without authentication or throttling, allowing model weight reconstruction. Use when writing inference API endpoints, deploying LLM-serving infrastructure, implementing model access controls, or configuring rate limiting and authentication for model endpoints. |
Prevents unauthorized replication of proprietary models through API abuse. Unauthenticated or unthrottled inference endpoints let attackers systematically query a model to reconstruct its weights or distill a clone — stealing the commercial and IP value of the deployment.
logprobs or full embedding vectors, enabling extractionFlag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties:
Translate each principle to the serving framework, auth provider, and rate-limiter of the audited file. Use the framework's documented authentication and throttling middleware — do not roll your own.