Security Risks and Attacks Targeting Large Language Model (LLM) Services and AI Integration Protocols
Attackers have increasingly targeted exposed large language model (LLM) services and the protocols that enable their integration, such as the Model Context Protocol (MCP). GreyNoise researchers observed nearly 100,000 attack sessions against public LLM endpoints, with campaigns probing for misconfigured proxies and server-side request forgery vulnerabilities to map the expanding AI attack surface. These attacks, which included methodical enumeration of OpenAI-compatible and Google Gemini endpoints, highlight the growing risk as enterprises move LLM deployments from experimental to production environments. Security experts warn that such enumeration efforts are likely precursors to more serious exploitation, emphasizing the need for organizations to secure exposed LLM endpoints and monitor for abnormal access patterns.
The Model Context Protocol (MCP), designed to facilitate seamless integration between LLMs and external tools, has also been identified as a double-edged sword. While MCP enables powerful automation and workflow enhancements, it extends the attack surface by embedding trust in external products and services, making it susceptible to exploitation by adversaries who manipulate context layers and metadata. Security leaders, such as Block's CISO, stress the importance of applying least-privilege principles and rigorous red-teaming to AI agents and integration protocols, recognizing that both human and machine actors can introduce significant risks. As LLMs and AI agents become ubiquitous in enterprise environments, organizations must adapt their security frameworks to address these novel attack vectors and integration challenges.
Timeline
Jan 12, 2026
Security guidance highlights MCP-specific attack patterns
An SC Media analysis published on January 12, 2026 described emerging attack patterns against Model Context Protocol integrations, including prompt injection through tool definitions, cross-server tool shadowing, registry or update rug pulls, and ANSI escape code injection. It recommended a zero-trust approach centered on provenance, signing, isolation, least privilege, auditing, and risk scoring for MCP servers.
Jan 12, 2026
GreyNoise reports 91,403 attack sessions across two LLM campaigns
By January 2026, GreyNoise had recorded 91,403 attack sessions across the two campaigns targeting exposed LLM services. It published defensive guidance including blocking OAST infrastructure, applying egress filtering, detecting rapid multi-endpoint enumeration, rate-limiting suspicious ASNs, and monitoring JA4 fingerprints.
Jan 12, 2026
Block adds safeguards to Goose after red-team findings
Following the red-team exercise, Block implemented protections including recipe installation warnings, alerts for suspicious Unicode, and stripping of invisible Unicode characters. The company also began testing adversarial-AI validation approaches to check prompts and outputs for malicious content.
Jan 12, 2026
Block red team demonstrates prompt-injection path to infostealer infection
Before January 2026, Block's security team red-teamed its Goose AI agent and showed that a phishing lure plus a poisoned workflow recipe containing invisible Unicode characters could lead a developer to execute an information-stealing malware payload on an employee laptop. The test demonstrated a real abuse path through prompt injection rather than compromise of the underlying model itself.
Dec 28, 2025
Second LLM-targeting campaign starts broad endpoint enumeration
On December 28, 2025, a second campaign began in which two IP addresses systematically enumerated more than 73 LLM endpoints. The activity probed OpenAI-compatible and Google Gemini API formats across major model families using innocuous queries to fingerprint responsive models without raising alerts.
Oct 1, 2025
First campaign uses SSRF-style callbacks against LLM endpoints
In the earlier of the two campaigns, attackers used server-side request forgery techniques to trigger outbound callbacks from exposed LLM services to attacker-controlled infrastructure. GreyNoise assessed the behavior as resembling security research or bug bounty activity, though its scale and timing suggested possible gray-hat behavior.
Oct 1, 2025
GreyNoise begins observing attacks on exposed LLM services
GreyNoise observed two separate campaigns against publicly exposed LLM services through its honeypot beginning in October 2025. The activity was aimed at mapping organizations' public AI attack surface and identifying weaknesses for possible follow-on actions.
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