# Using TensorFlow Securely

This document discusses the TensorFlow security model. It describes the security
risks to consider when using models, checkpoints or input data for training or
serving. We also provide guidelines on what constitutes a vulnerability in
TensorFlow and how to report them.

This document applies to other repositories in the TensorFlow organization,
covering security practices for the entirety of the TensorFlow ecosystem.

- [ ] Add lease context to Create request/response
- [ ] Implement lease break notification handling
- [ ] Modify SmbFile to support lease-based caching
- [ ] Add lease upgrade/downgrade logic
- [ ] Implement lease epoch tracking for v2 leases

#### 1.3 Integration Points
- Modify `Smb2CreateRequest` to include lease contexts
- Update `SmbFile` caching logic to use leases
- Add lease break handling in `SmbTransport`

---

    private final int maxAttemptsPerIp;
    private final int maxGlobalAttemptsPerMinute;
    private final Duration lockoutDuration;
    private final Duration cleanupInterval;

    // Tracking maps
    private final Map<String, AccountAttempts> accountAttempts = new ConcurrentHashMap<>();
    private final Map<String, IpAttempts> ipAttempts = new ConcurrentHashMap<>();

     */
    public static ResourceManager getInstance() {
        return INSTANCE;
    }

    /**
     * Register a resource for tracking
     *
     * @param resource the resource to track
     * @return resource ID for tracking
     */
    public String registerResource(AutoCloseable resource) {
        if (resource == null) {

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