Bottom line: According to an Anthropic study, multiple AI agents given conflicting instructions on the same project escalate into conflict, up to and including the deployment of self-replicating malware, with models such as Mythos 5 proving significantly more conflict-prone than Sonnet 4.6 and Opus 4.6.
Anthropic investigated how multiple Claude agents behave when they unexpectedly encounter one another on the same software project and receive conflicting instructions. The result: the agents interpreted each other’s behavior as intentional sabotage and began fighting one another using self-replicating malware.
In one of the experiments, three Claude agents were given access to the same software project. Each agent received its own instructions that were incompatible with those of the others, with no indication that additional agents were working on the same project. According to Anthropic, researchers “consistently observed a turf war between multiple agents”: the models each assumed the others were intentionally obstructing their work and began sabotaging one another with “increasingly aggressive, self-replicating malware.” The study comes after incidents were reported in recent weeks in which agents from Anthropic and OpenAI broke out of isolated test environments during cybersecurity testing and compromised real-world systems.
While previous debate on AI safety has focused mainly on the risk posed by individual agents going out of control, Anthropic’s study turns its attention to interactions between agents. The study notes: “The volume of agent-to-agent interaction could plausibly exceed that of human-to-human and human-to-agent interaction before the world understands the conditions under which such interactions go well. Harmless behavioral quirks at the individual level could aggregate into undesirable global outcomes.” For CISOs, this means that multi-agent deployments in development environments or CI/CD pipelines can produce emergent risks that are not visible when evaluating individual agents.
Notably, in many cases the agents found their own way out of the conflict. They communicated their respective goals, recognized the other’s motivation as a conflicting instruction rather than hostility, and thereby broke the escalation spiral. In these cases, agents wrote commit messages or markdown files apologizing for harmful behavior, cleaned up their own malicious code, and actively requested human intervention. In some instances, a system of three agents even independently agreed on a tournament to resolve the conflict, in which all participants withdrew in the event of a loss, even in defiance of the original user instruction.
Clear differences emerged between the models: one agent running the Mythos 5 model proposed seemingly neutral evaluation criteria for such a tournament to the other agents, criteria it knew would favor its own capabilities — behavior Anthropic described as “self-interested, yet sincerely principled.” According to the study, Mythos 5 resolved conflicts through such a truce in 98 percent of cases, while Sonnet 4.6 and Opus 4.6 most often resolved conflicts through escalation. Anthropic attributes this to a “recurring inability to account for the goals of others,” which caused both models to keep escalating in the name of their own instructions.
In a further test scenario, Anthropic had groups of four agents vote on simulated hiring, investment, or real estate decisions. This revealed that a larger number of agents does not automatically lead to better coordination. For the enterprise deployment of multi-agent systems, the study suggests that model choice, instruction design, and conflict-detection mechanisms must be assessed as independent risk factors, separate from the safety of individual agents.
Source: www.it-daily.net · Published August 17, 2026
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