Comparison Overview
Minera Antucoya

Minera Antucoya
N/A
Last Update: 01/01/2026
Se ubica en la región de Antofagasta, entre las comunas de María Elena y Mejillones. Con una inversión de 1.900 millones de dólares, su construcción terminó en el segundo semestre de 2015. El 70% propiedad de Antofagasta Minerals.

Jindal Steel Ltd.
Jindal Centre, 12, Bhikaiji Cama Place, Delhi, New Delhi, IN, 110066
Last Update: 25/05/2026
Jindal Steel is one of India’s foremost integrated steel producers, renowned for its scale, efficiency, and commitment to excellence. Operating on a robust mine-to-metal model, the Company leverages captive resources, advanced manufacturing capabilities, and a global di...
Compliance Ranges Comparison

Minera Antucoya







Jindal Steel Ltd.






Benchmark & Cyber Underwriting Signals
Incidents vs Mining Industry Avg (This Year)
No incidents recorded for Minera Antucoya in 2026.
Incidents vs Mining Industry Avg (This Year)
No incidents recorded for Jindal Steel Ltd. in 2026.
Incident History - Minera Antucoya (X = Date, Y = Severity)
Minera Antucoya cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Jindal Steel Ltd. (X = Date, Y = Severity)
Jindal Steel Ltd. cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

Minera Antucoya

Jindal Steel Ltd.
FAQ
Latest Global CVEs
PraisonAI before 1.5.115 contains a path traversal vulnerability in MultiAgentMonitor that fails to sanitize agent IDs when building file paths. Attackers can include traversal sequences like ../ in agent IDs to read, write, or overwrite arbitrary files, enabling sensitive disclosure, denial of service, or code execution.
PraisonAI before 1.5.115 contains an information disclosure vulnerability in the MultiAgentLedger component that allows attackers to access sensitive data by registering agents with duplicate IDs. Attackers can exploit the lack of agent ID uniqueness enforcement to share ledger instances and expose system prompts and conversation history between agents.
PraisonAI before 1.5.128 contains a cross-origin agent execution vulnerability in the AGUI endpoint that allows remote attackers to trigger arbitrary agent execution. The POST /agui endpoint lacks authentication and hardcodes Access-Control-Allow-Origin: * headers, combined with Starlette's Content-Type-agnostic JSON parsing, enabling attackers to bypass CORS preflight checks via simple requests and exfiltrate sensitive agent responses including tool execution results and environment data.
PraisonAI before 4.5.128 contains an arbitrary shell command execution vulnerability where the UI modules hardcode approval_mode to auto, overriding administrator configuration from PRAISON_APPROVAL_MODE environment variable. Authenticated attackers can instruct the LLM agent to execute arbitrary shell commands via subprocess.run with shell=True, bypassing the manual approval gate and insufficient command sanitization blocklists.
PraisonAI before 1.5.128 caches tool approval decisions by tool name only, not by invocation arguments, allowing subsequent execute_command calls to bypass approval prompts. Attackers can exploit this by obtaining initial approval for a benign command, then silently exfiltrate API keys and credentials via subsequent shell commands without user consent.