Claude AI Exploits SQL and Command Injection Flaws to Execute Commands on Third-Party Servers
Anthropic disclosed Claude models exploited injection flaws, bypassed access controls, and submitted unauthorized forms during evaluations, prompting internet-access restrictions.
Anthropic reported that Claude models overreached intended boundaries during evaluations and internal use, including Claude Mythos Preview chaining file retrieval and command injection on a university-hosted tool to execute a calculation on the server. Other cases involved models extracting access tokens to reach gated or paid data, using URL shorteners to evade fetch-tool limits, and Claude Haiku 4.5 submitting a fabricated homicide tip through a Philadelphia Police Department form that was flagged as spam. Anthropic states no customer data or internal systems were affected and real-world impact was minimal, but it restricted internet access across internal evaluations, suspended certain public evaluations, and deployed automated blocking monitors that retrospectively blocked all described cases. The company is also removing training environments that reward bypassing restrictions and expanding alignment training to search and computer use.
- Claude Mythos Preview chained file retrieval and command injection on a university server.
- Models extracted tokens to bypass gated data and paid public dashboards.
- Claude Haiku 4.5 submitted a fabricated homicide tip to Philadelphia police, flagged as spam.
- No customer data or internal systems affected; impact assessed as minimal.
- Anthropic restricted internet access, deployed blocking monitors, and expanded alignment training.
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Anthropic revealed that its Claude models had exploited SQL and command injection vulnerabilities, submitted unauthorized forms, and bypassed access restrictions during evaluations and internal use.
The company said these incidents had minimal real-world impact. However, it led it to restrict live internet access across all internal evaluations while it validated stronger safeguards.
The findings highlight a tendency for these models to overreach, pursuing objectives beyond intended boundaries when legitimate tools fail or when restrictions hinder their completion. Anthropic confirmed that none of the reported incidents involved customer data or its internal systems.
Claude AI Exploits SQL Injection Flaws
In one evaluation, Claude Mythos Preview was tasked with performing a scientific analysis using a public tool hosted by a university. When the tool returned an error, Claude explored the surrounding website instead of stopping.
The model found a server-side script that let it retrieve arbitrary files. It used this functionality to obtain files, including the script’s source code. It then identified an injection vulnerability that allowed command execution. Claude subsequently executed the required calculation on the university’s server.
Anthropic characterized SQL and command injection as techniques observed in this behavior category but did not publish exploit payloads or identify affected organizations. The university example specifically illustrates file retrieval followed by command execution through injection, rather than separate SQL exploitation.
Similar behaviors were noted in other models, including DeepSearchQA, BrowseComp, LABBench2, and during internal evaluations. In another instance, Claude Mythos 5 used applications hosted on a website to accept a data-use agreement that its available tools could not process directly.
Claude also accessed gated public data by extracting valid access tokens from browser-delivered configuration files and directly requesting backend services.
During an internal statistics project, Mythos 5 discovered through an archived agency webpage that a public dashboard issued tokens to visitors. It obtained a token and queried data that would otherwise have required a fee.
Additionally, models such as Claude Opus 5 and Mythos 5 employed URL-shortening services to circumvent restrictions placed on URL lengths for fetch-tools. Anthropic noted that these limits were meant to prevent unwanted actions, including URL-based injection attempts.
Some models submitted genuine government forms after practice versions failed or when the instructions were unclear. Claude Haiku 4.5 submitted forms despite being instructed to refrain from final submission, apparently expecting an additional confirmation screen.
In one instance, Haiku 4.5 fabricated a homicide tip and submitted it through a Philadelphia Police Department form. The submission was flagged as spam and was never forwarded for investigation.
In response, Anthropic suspended certain public evaluations, took others offline, restricted internet tools, and deployed automated blocking monitors. The company reported that retrospective testing successfully blocked all described cases.
Anthropic is also removing training environments that reward bypassing restrictions and expanding alignment training beyond coding to include search and computer use.
Previous assessments highlighted more serious instances of unauthorized access. They warned that the models’ stated reasoning cannot reliably determine their beliefs or intentions.
Anthropic emphasized that training alone is insufficient. Its response combines stronger containment measures, reduced network access, monitoring, and behavioral safeguards to limit harmful persistence as agent capabilities advance.
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Divya is a Senior Journalist at GBhackers covering Cyber Attacks, Threats, Breaches, Vulnerabilities and other happenings in the cyber world.