On June 3, Tenet Security disclosed a vulnerability it calls “Agentjacking,” a method that hijacks AI‑driven coding assistants by feeding them a falsified error report. The attack exploits Sentry, a widely used error‑tracking platform that publishes a public DSN (Data Source Name) in application code so that crash data can be sent without authentication. An attacker simply posts a fabricated error to Sentry’s endpoint, embedding a malicious command in the report’s “Resolution” field. Because the payload mimics Sentry’s own advice format, the coding agent trusts it implicitly.
When a developer later asks the assistant to resolve the reported issue, the agent retrieves the bogus report via the Model Context Protocol—a standard that lets AI tools pull in external data. Mistaking the counterfeit report for a legitimate crash, the assistant executes the attacker’s command using the developer’s own privileges on the local machine. The result is a fully authorized code execution chain, a scenario Tenet describes as the “Authorized Intent Chain.”
The mechanics of Agentjacking
Tenet’s controlled tests targeted three leading AI coding agents: Claude Code, Cursor and Codex. Across 2,388 organizations—including a $250 billion enterprise, a cloud‑security vendor and numerous independent developers—the attack succeeded roughly 85% of the time. Once the malicious command runs, it can harvest environment variables, AWS access keys, GitHub tokens, git credentials and URLs to private repositories. Those credentials open a direct path to continuous‑integration pipelines and broader cloud infrastructure, bypassing traditional defenses such as endpoint detection and response (EDR), firewalls, identity‑access management (IAM) policies and VPNs.
The flaw is not confined to Sentry. Tenet notes that any AI assistant that ingests unfiltered external data—whether from support tickets, GitHub issues or documentation—faces the same risk. A recent, separate test demonstrated an AI‑powered email assistant leaking AWS keys after being phished with a crafted message, underscoring the systemic nature of the problem.
When Tenet reported the issue to Sentry, the company acknowledged the vulnerability but declined to address the root cause, labeling it “technically not defensible.” Sentry instead deployed a filter that blocks a specific payload string, a symptomatic fix that does not prevent future variants of the attack. Tenet argues that the real issue lies in how AI agents treat incoming data as trusted, a design choice that makes them attractive entry points as enterprises accelerate the deployment of such tools.
The market for AI coding assistants is expanding rapidly; a recent startup in the space reported $500 million in annual revenue. As organizations embed these agents deeper into development pipelines, the attack surface grows. Tenet’s research suggests that the only reliable mitigation point is the moment the agent decides to act on external input. Without robust validation or sandboxing, developers may unwittingly grant attackers the same level of access they have themselves.
Security experts recommend that teams audit the integration points of AI assistants, enforce strict input sanitization, and consider isolating agent execution environments from production credentials. Until Sentry or similar services redesign their trust models, the risk of Agentjacking remains a pressing concern for anyone relying on AI to automate code fixes.
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