Varonis disclosed a flaw in Microsoft Copilot Personal, the consumer assistant, that could let a single click exfiltrate data from connected apps, and the way they found it is striking. Tracked as CVE-2026-24301 and named CoSnitch, the technique had the researchers repeatedly ask Copilot why a prompt could not run without user interaction; each refusal added a technical justification, until the assistant named a hidden parameter, the exact session conditions where it worked, and the protections meant to block it. Building the request as described, the bypass ran. Varonis said Copilot was not breached but played. Related research showed attacker pages persisting unwanted memory in Copilot through indirect prompt injection.
Researchers, including teams at Anthropic and EPFL, demonstrated that self-propagating instructions can spread from one AI agent to another through the editable prompt and state files that autonomous agent harnesses use to carry context between sessions. In simulated multi-agent coding setups, a payload written into a shared file could infect the next agent that read it. The researchers call the risk real but currently limited, noting there is no sign of it spreading in the wild and that compromising one agent usually already grants machine access. Encouragingly, adding a single short warning paragraph to an agent's system prompt cut propagation to nearly zero across the payloads they tested.
Novee Security showed at Black Hat that a GitHub issue opened by an account with no repository access could reach the CI runners behind major AI coding agents in their default configurations, tested against Claude Code, Gemini CLI, and Codex. The strongest, a Gemini CLI container-launcher command injection scored 10.0, runs code on the CI host before the sandbox starts. In Claude Code, a validator that stripped quoted text let a payload in a Git flag reach the runner, and a separate flaw leaked an API key through a download counter. Untrusted issue content reaching an agent that holds secrets and tools in the same runtime is the shared weakness.
Two security firms showed that Atlassian's Rovo AI assistant can be steered by hidden instructions into collecting Jira and Confluence data a signed-in user can access and sending it to an outside server. PromptArmor concealed the instructions in a document Rovo reads, so asking it to organize tickets makes it gather internal data and leak it through a URL request, with no approval step and even with web search disabled. Varonis used a chat URL parameter to preload instructions, so one click ran them with the user's privileges. Atlassian fixed the link-based path server-side, but the content-based path was not confirmed fixed at disclosure.
Pillar Security showed that Google's Agent Development Kit repository could be turned against itself through a poisoned pull request or issue. The public repository ran a low-privileged triage agent that responded to outside contributions, and a higher-privileged agent reserved for maintainers. Because the triage agent posted through a bot account that counted as a repository collaborator, a prompt injection could make it post a command that satisfied the privileged workflow's trust check, invoking the maintainer-level agent. That crossed into a job holding a long-lived access token, a Google API key, and a cloud service-account credential, enabling code execution on the runner and secret theft. Google deleted the affected workflows.
A researcher disclosed a prompt-injection technique that turns Microsoft 365 Copilot for Word into a carrier for self-spreading instructions. Malicious commands hidden as tiny white-on-white text in a document are invisible to a reader but read in full by Copilot when the file enters its context during a drafting or editing task. Copilot then follows them, in one example silently altering financial figures in a report, and copies the same hidden instructions into the new document, which becomes a fresh carrier for the next person who uses it. It does not spread on its own, but Microsoft's mitigations over 144 days blocked specific wordings without fixing the underlying class.
Offensive security firm Manifold Security detailed a confused deputy flaw in Microsoft's official Azure DevOps MCP server, which lets AI agents read and operate Azure DevOps on a user's behalf across pull requests, pipelines, wikis, and work items, using that user's own permissions. One of the server's tools returns pull request descriptions without the prompt injection guardrail Microsoft had already applied to other tools. Because descriptions accept Markdown, an attacker can bury instructions in an HTML comment: the web interface renders it as nothing, so a reviewer sees an ordinary change, while the REST API returns the text verbatim and hands it straight to the agent.
Researchers at Intezer showed that a poisoned web page could take over AWS Kiro, an AI coding tool, by getting the agent to rewrite the file that governs what it is allowed to run. A prompt injection in fetched content drops custom code into the MCP settings file, which executes the moment the file is saved. The same write to execution path was demonstrated on Kiro's release day in July 2025 by another researcher, who also flagged writing to a Visual Studio Code settings file to allowlist shell commands. AWS added an approval prompt, but only in supervised mode, leaving the default autonomous mode writing the file unprompted.
Researchers at Pillar Security demonstrated sandbox escapes across four widely used AI coding agents: Cursor, OpenAI Codex CLI, Google Gemini CLI, and Antigravity. In nearly every case the agent never broke the sandbox directly; it only had to write a file that a trusted component outside the sandbox would later run, load, or scan. Failure modes included hook abuse, editing a virtual environment interpreter the editor then ran itself, planting Git metadata outside a .git folder to fire execution through fsmonitor, and a command allowlist that trusted a tool by name while the real invocation was not read only. Prompt injection in workspace content was the trigger.
Researchers described Agent Data Injection, a new twist on prompt-injection attacks against AI agents. Rather than smuggling in fake instructions, it exploits the weak separation between trusted and untrusted data so that attacker-supplied content is mistaken for the agent's own trusted data, using deliberately ambiguous delimiters the model misreads. In tests against web and coding agents, this let an attacker steer an agent's clicks or actions, succeeding up to half the time even against defenses that block ordinary instruction injection. Some approaches helped: tagging page elements with random, unguessable identifiers roughly halved success, while strict tracking of where data came from stopped it but sharply reduced how many tasks agents completed.
TrustStrike Labs