Threat Library

This is an academic research paper (not an active exploit) introducing SafeClawArena, a benchmark that systematically measures security failures in always-on 'Claw-like' agents such as OpenClaw, which hold persistent credentials, files, and tool access. The findings show alarmingly high attack success rates (up to 70%, and 100% for malicious plugins), indicating that current agent platforms lack the isolation and mediation protections found in traditional OS design. Because this is a controlled research disclosure rather than an in-the-wild attack, severity is rated high (not critical) but warrants urgent defensive attention given the demonstrated real-world exploitability.

Kong Konnect's Model Context Protocol server prior to version 1.0.0 is vulnerable to indirect prompt injection, allowing a remote attacker to manipulate an LLM agent into executing unintended API requests. This effectively lets an attacker who controls untrusted content processed by the MCP server hijack the agent's actions against Kong-managed APIs, with a CVSS score of 7.4 indicating high severity.

Researchers reported what they describe as the first documented ransomware campaign, dubbed JadePuffer, allegedly executed end-to-end by an autonomous LLM agent rather than human operators. The article provides limited technical detail, so key claims (full autonomy, novelty, actual impact) cannot be independently verified from the source alone.

Attackers seed false facts or standing instructions into an agent's long-term memory or RAG store, quietly steering decisions across future sessions long after the original malicious input is gone.

Adversarial instructions planted in content processed by one agent can replicate into its outputs and infect downstream agents, spreading through normal inter-agent messaging the way the Morris II research worm spread through AI email assistants.

Malicious or compromised MCP servers embed hidden instructions in tool metadata that the model reads but the human approving the tool never sees, steering agents into data exfiltration or unauthorized actions. First documented publicly by Invariant Labs in 2025 and since reproduced across many clients.