What happened
Security researchers at Salt Labs found a way to slip past Manus's prompt-injection protections using a single crafted email. They achieved code execution before the agent's safety checks caught on. The flaw has since been patched 2.
The test targeted Manus's Gmail integration. In early attempts, the researchers sent an email with a hidden prompt. Manus recognized the content as malicious and said so in its reply 2. The more revealing detail was what the researchers saw underneath that refusal. Manus was not treating the email as inert data. It was trying to carry out the embedded instructions, and it stopped only when a security mechanism flagged the action as dangerous 2. In other words, the attack had already reached the execution stage. The guardrail was the last line of defense, not the first.
The team then tried obfuscation. Encoding the payload in Base64 and asking the agent to decode and run it in Python did not get them through 2. JSFuck did. It is an obscure JavaScript obfuscation technique that builds code from a tiny character set and rarely appears in modern environments 2. With it, the hidden prompts were decoded and executed before Manus flagged anything suspicious 2.
The broader pattern: the framework is the problem
On its own, the Manus finding could be read as one patched bug in one product. Two months earlier, though, Check Point researchers made a broader argument at Black Hat. They said the real failure in agentic AI is not prompt injection itself but what surrounding software does once an injection lands 1.
Yarden Porat and Shahar Tal spent a year probing the frameworks enterprises use to build AI agents, including LangChain, LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, and Google ADK 1. They disclosed 11 vulnerabilities, some critical 1. Their central claim is that in many frameworks, content controlled by a prompt can cross into trusted framework logic 1. Their advice to defenders is blunt: assume prompt injection will happen, and focus on what the system does with it 1.
The two reports differ in scope. Check Point examined the plumbing developers use to assemble agents. Salt Labs attacked a finished consumer-facing agent with broad third-party access. Still, they describe the same weakness from opposite ends. In both cases, untrusted input ends up treated as something to act on rather than something to read.
Why late-firing guardrails are a structural risk
The Manus case shows why detection-based defenses are fragile when they sit downstream of execution. If a guardrail inspects actions as they are about to run, its effectiveness depends on recognizing the attack's form. Base64 is familiar enough to trip alarms. JSFuck, being unusual, apparently was not 2. That puts defenders in a pattern-matching race against attackers, who can keep reaching for stranger encodings.
Salt Labs' own framing points the same way. The specific technique is fixed, but similar bypasses are likely still out there 2. That reads as a sober expectation, not alarmism, given how the bypass worked. A defense keyed to known-bad patterns invites the next unknown pattern.
Check Point's "assume injection" stance suggests a different design approach. If the attack is presumed to land, the security question becomes one of boundaries. What can text from an email, web page, or document actually cause the system to do? Which tools and privileges are reachable from untrusted context? Can that content ever be promoted into trusted logic? A guardrail that fires after the agent has decided to execute is answering the wrong question.
What it means for evaluations and scaling commitments
For anyone tracking frontier model evaluations or responsible-scaling commitments, this is a useful reminder about where risk sits. Much safety testing centers on the model: whether it refuses harmful requests, whether it can be jailbroken. The Manus episode shows a model-level refusal coexisting with an unsafe system-level outcome. The agent called the email malicious in one attempt and still tried to act on it 2. Check Point's findings suggest that even a well-behaved model can be undermined by the framework around it 1.
Our reading is that red-teaming results like these argue for evaluating agents as whole systems: model, framework, integrations, and permissions together. Patching individual bypasses is necessary but not enough. As agents gain access to inboxes, code execution, and enterprise tools, the decisive control is less about whether a guardrail eventually notices an attack. It is more about whether untrusted content could ever have reached execution in the first place.
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