Capabilities Abuse

Capabilities Abuse is a privilege escalation sub-technique where attackers exploit improperly configured capability settings in Unix-like systems to elevate privileges beyond intended boundaries. During the Deepening Control phase, after initial access has been established, attackers seek to leverage the fine-grained permissions provided by Linux capabilities (controlled via the cap_set_proc, cap_get_proc functions and managed by setcap/getcap tools) to perform operations that would typically require root access. These capabilities, which fragment the omnipotent root privilege into discrete permissions (such as CAP_NET_ADMIN for network configuration changes, CAP_SYS_ADMIN for system administration tasks, or CAP_DAC_OVERRIDE for bypassing file permission checks), can be abused when inappropriately assigned to executables, processes, or containers. A common attack vector involves exploiting binaries with the CAP_SETUID capability to spawn privileged shells, or leveraging CAP_DAC_READ_SEARCH to access sensitive system files despite lacking standard file permissions. Unlike traditional privilege escalation that requires full root access, capabilities abuse allows attackers to gain specific elevated powers that may suffice for their malicious objectives while potentially evading detection methods focused solely on complete privilege elevation.

Examples in the Wild

Notable Capabilities Abuse Attacks:

ShadowRay Attack The ShadowRay attack exploited misconfigured capabilities in Ray's distributed training infrastructure. Attackers leveraged CAP_DAC_READ_SEARCH capabilities granted to training processes to access model weights and training data across nodes, demonstrating how capabilities abuse can enable intellectual property theft in AI infrastructure.

ShellTorch (CVE-2023-43654) The ShellTorch attack included capabilities abuse components in its attack chain. Attackers exploited overly permissive capabilities granted to TorchServe processes to access sensitive model files and system resources, enabling both model theft and remote code execution.

Ultralytics Model Registry Compromise The Ultralytics attack leveraged capabilities abuse to escalate privileges within the model registry infrastructure. Attackers exploited CAP_SYS_ADMIN capabilities to manipulate model artifacts and inject malicious code into the YOLOv8 ecosystem.

Attack Mechanism

Common Capabilities Abuse Techniques:

1. Training Process Exploitation

   # ShadowRay-style capability abuse
   def exploit_training_process():
       # Abuse CAP_DAC_READ_SEARCH
       training_pid = find_training_process()
       capabilities = get_process_capabilities(training_pid)
   
       if "CAP_DAC_READ_SEARCH" in capabilities:
           # Access model weights across nodes
           for node in cluster_nodes:
               weights = read_file(f"/proc/{training_pid}/root/model/weights.pt")
               exfiltrate_data(weights)
   

2. Model Server Compromise

   # ShellTorch-style capability escalation
   def exploit_model_server():
       # Abuse CAP_SYS_ADMIN
       server_pid = find_torchserve_process()
       if has_capability(server_pid, "CAP_SYS_ADMIN"):
           # Manipulate model serving
           inject_malicious_model()
           modify_runtime_behavior()
   

3. Registry Access Abuse

   # Ultralytics-style registry exploitation
   def exploit_registry():
       # Abuse excessive capabilities
       if process_has_capability("CAP_DAC_OVERRIDE"):
           # Modify model artifacts
           for model in registry.list_models():
               inject_backdoor(model)
               update_registry(model)
   

Detection Challenges

Why Traditional Security Tools Fail:

1. Fine-Grained Permissions

   # Capability granularity
   process_capabilities:
     - CAP_NET_ADMIN: "network ops"
     - CAP_SYS_ADMIN: "system ops"
     - CAP_DAC_OVERRIDE: "file access"
     # Which are actually needed?
   

2. Container Complexity

   # Container capabilities
   container_security:
     - default_caps: "too permissive"
     - custom_caps: "misconfigured"
     - inherited_caps: "unexpected"
     # How to track capability flow?
   

3. ML Infrastructure Specifics

   # ML process requirements
   ml_capabilities:
     - training: "needs file access"
     - serving: "needs network"
     - monitoring: "needs metrics"
     # What's the minimum set?
   

Required Application Security Strategy:

# Capability monitoring rules
- rule: "Suspicious Capability Usage"
  condition: |
    process.capability_abuse_detected OR
    process.unexpected_capability_used OR
    process.capability_escalation
  severity: critical

# Container security
- rule: "Container Capability Control"
  condition: |
    container.excessive_capabilities OR
    container.capability_drift OR
    container.privilege_escalation
  severity: high

# ML process protection
- rule: "ML Infrastructure Security"
  condition: |
    ml_process.unauthorized_access OR
    ml_process.capability_abuse OR
    ml_process.permission_escalation
  severity: critical

Key Detection Requirements:

1. Capability Monitoring
2. Process capability tracking
3. Usage pattern analysis

4. Escalation detection

5. Container Controls

6. Capability minimization
7. Runtime enforcement

8. Drift detection

9. ML-Specific Controls

10. Training process isolation
11. Model access control
12. Resource permission management