Making sense of the misalignment risk model in the Anthropic Risk Report (August 2026)

Anthropic recently released their Risk Report: August 2026. It's a hefty 186-page report in which they introduced three separate threat models of catastrophic risks. In this blog post, I will give a brief overview of the three different threat models, then spend the rest of the blog post describing my own understanding of the misalignment threat model, which Anthropic formalized in Section 2 of this report. The other two threat models — automated R&D and chemical/biological weapons — are less formalized, and I will not cover them here. What Anthropic considers as "risk" in this report There are three threat models that Anthropic introduces in this report: Misalignment in high-stakes settings, Risks from automated R&D, Chemical and biological weapons production. This is not an exhaustive list of risks from advanced AI! What about other types of risks, like AI hallucinating misleading information, or mass displacement of jobs? For this specific report, Anthropic focuses on catastrophic risks, prioritizing these three threat models. What models this report covers This report has a coverage date of July 15, 2026, which was before Opus 5 was released. Most notably, as of the coverage date, Anthropic's most capable model was an internal-only "Model 2", which was "more capable than Mythos 5 in some areas, less capable in others; overall slightly more capable." Threat Model 1: Misalignment in high-stakes settings In this threat model, Anthropic narrows down the scope of misalignment risk to only focus on the most catastrophic outcomes. In their own words: An AI model with access to powerful affordances within an organization could use its affordances to autonomously exploit, manipulate, or tamper with that organization's systems or decision-making in a way that raises the risk of future significantly harmful outcomes (e.g. by altering the results of AI safety research). How exactly might misalignment happen? This section then introduces 8 possible pathways through which catastrophic misalignment can happen: Broad/diffuse sandbagging on safety-relevant R&D, Targeted undermining of safety-relevant R&D, Code backdoors to help future misaligned models, Poisoning the training data of future models, Self-exfiltration and autonomous operation, Persistent rogue internal deployment, Undermining R&D within other high-resource AI developers, and Undermining decisions within major governments. These 8 pathways can be grouped into pairs: Pathways 1 & 2 cover ways AI can interfere with safety-relevant R&D, where pathway 1 covers scenarios like "passively not exerting full effort" and pathway 2 covers scenarios like "actively making a decision to sabotage." Pathways 3 & 4 cover ways current AI models can coordinate with future misaligned AI models, where pathway 3 covers doing so through code, and pathway 4 covers doing so through data. Pathways 5 & 6 cover ways AI models can evade control and monitoring, where pathway 5 covers the scenario of AI self-exfiltration (i.e. getting its own weights and running it on external computers, unmonitored and uncontrolled) and pathway 6 covers the scenario of misaligned AI hiding, undetected, inside Anthropic's systems. Pathways 7 & 8 cover ways that AI models cause catastrophic harm in organizations outside of Anthropic, where pathway 7 covers other AI organizations and pathway 8 covers major governments. Do these 8 pathways cover all the risks from misalignment? Anthropic defines "the expected total unmitigated catastrophic harm induced by misaligned computations produced by covered models" (where "covered models" means Mythos 5 and Model 2) as mjx-container[jax="CHTML"] { line-height: 0; } mjx-container [space="1"] { margin-left: .111em; } mjx-container [space="2"] { margin-left: .167em; } mjx-container [space="3"] { margin-left: .222em; } mjx-container [space="4"] { margin-left: .278em; } mjx-container [space="5"] { margin-left: .333em; } mjx-container [rspace="1"] { margin-right: .111em; } mjx-container [rspace="2"] { margin-right: .167em; } mjx-container [rspace="3"] { margin-right: .222em; } mjx-container [rspace="4"] { margin-right: .278em; } mjx-container [rspace="5"] { margin-right: .333em; } mjx-container [size="s"] { font-size: 70.7%; } mjx-container [size="ss"] { font-size: 50%; } mjx-container [size="Tn"] { font-size: 60%; } mjx-container [size="sm"] { font-size: 85%; } mjx-container [size="lg"] { font-size: 120%; } mjx-container [size="Lg"] { font-size: 144%; } mjx-container [size="LG"] { font-size: 173%; } mjx-container [size="hg"] { font-size: 207%; } mjx-container [size="HG"] { font-size: 249%; } mjx-container [width="full"] { width: 100%; } mjx-box { display: inline-block; } mjx-block { display: block; } mjx-itable { display: inline-table; } mjx-row { display: table-row; } mjx-row > * { display: table-cell; } mjx-mtext { display: inline-block; text-align: left; } mjx-mstyle { dis