Your phone rings, you pick up and say hello. On the other end: total silence. No one answers, and the call abruptly disconnects. If you don’t already use spam call blockers, you’ve almost certainly run into this situation before.

In most cases, these are scam calls. Today, we explain why these calls happen, what the callers want from you, and how to protect yourself. Most importantly, we’ll look at whether you even need to bother protecting yourself against them in the first place.

Who’s calling?

It’s not just scammers on the line — robots, legitimate call center operators, and ordinary folks make these calls too. Let’s break down each type of caller — ordered from best-case to worst-case scenario for your security.

Actual person

The most harmless scenario is that an actual person called you, but their microphone is acting up. Maybe they accidentally muted themselves with their ear, or their smartphone connected to a Bluetooth headset, speaker, or car system that isn’t capturing their voice. Carrier glitches can also mute one side of a call. The caller might have no idea there’s a problem — as far as they know, they are speaking, but no one can hear them. In cases like this, you usually recognize the incoming phone number.

If the call comes from an unknown number, there’s still no need to panic — though the list of those who might be calling gets much longer.

One legitimate possibility is a call center agent who simply didn’t pick up or connect their headset in time. Call center systems are designed to dial numbers faster than agents can wrap up their calls. The system tried to route the call to a human, but no reps were available. That’s why you sometimes have to wait a few seconds before hearing a single word, or why you might hear ringing tones as if you were the one making the call.

Robot or AI

Silence on the line is a common sign of robocalls. Robots test whether a phone number is active and, if it is, pass it along to a human — meaning a real sales rep (or scammer) will call you back in the next few days. It’s worth noting that scammers aren’t the only ones making these pinging calls. Legitimate call centers use the exact same tools to reduce the workload on their live agents.

An AI agent could also be behind the silent call. To the person answering, there’s no practical difference: the call looks identical to one made by a standard bot. However, AI can do more than just auto-dial numbers — it can analyze your response and use that data to decide whether your number is active and ready to be handed off to a live person for follow-up.

Unwanted caller

Now we get to the real threat. Perhaps one of the most dangerous and unpleasant sources of silent phone calls is a scammer. A quick, silent call like this can actually be the groundwork for a long, elaborate attack with cover stories about loans, government agencies, other fraudsters, even law enforcement.

Debt collectors might also be calling and staying quiet. Your number could end up on their radar if you, your family, or close contacts have outstanding debts. In these cases, a silent call is often used as a tactic for psychological pressure.

A similar technique is used in stalking. While silent calls cause no direct harm on their own, they can be leveraged to induce anxiety, create a feeling of being constantly watched, and cause ongoing emotional distress.

Why do they call and stay silent?

When you pick up, you likely respond out of habit with a quick “Hello?” or “Hi there.” That’s all it takes for the other party to gather a wealth of data. While this information used to be difficult to process, the rise of artificial intelligence has made the task significantly easier. Let’s look at what someone can learn about you from just one spoken word:

• Region, accent, and location. Scammers are sophisticated and cunning. Their tactics are often tailored by region — targeting residents of specific countries or even regions within them. This is especially relevant in places like India or South Africa, which have 22 and 11 official languages, respectively.
• Approximate age and gender. While a human listener might easily confuse a teenager’s voice with a young woman’s or misjudge someone’s age entirely, AI is far better at picking up on subtle vocal nuances. Knowing your age and gender helps scammers refine their playbook for future social engineering attacks.
• Times you’re available. If you answer the phone in the morning, afternoon, or late at night, attackers can schedule their follow-up call during the exact time window when you’re most likely to pick up.
• Likelihood of a successful attack. AI can automatically assess the potential value of a target. For instance, if someone answers quickly, speaks calmly, and doesn’t immediately hang up on unknown numbers, they’ll likely be assigned a higher priority for follow-up calls by live scam operators.

Back to the “why do they call and stay silent”, the main reason is to harvest biometric data. Just a few seconds of recorded audio can help cybercriminals create a voice deepfake. While one or two words might not yield a convincing clone on their own, attackers can stitch together recordings from multiple silent calls to build a believable replica.

This technology is already being used in real-world scams. Impersonating a relative, colleague, or boss, fraudsters can urgently ask you to send them money, to share a two-factor authentication code for government services, or to complete some other seemingly innocuous request. The more realistic the deepfake sounds, the harder it is to spot the scam — especially when backed by a convincing backstory.

What to do if you get a silent call?

If you answer a call, say a few words, and hang up, there’s no need to panic. However, that brief interaction can confirm to attackers that your number is active and that you’ll answer calls from unknown numbers. As a result, your phone number could end up on target lists for future spam or scam campaigns. That said, it’s important to remember that a single silent call poses no immediate security threat.

Here are a few tips to help you stay calm and avoid falling for scam tactics if those silent calls are becoming a problem:

• Don’t answer calls from unknown or hidden numbers. Here’s a helpful tip: if someone genuinely needs to reach you, they’ll find another way to do so, or keep calling from the exact same number at various times. Scammers almost always dial from different numbers, while automated bots operate on a rigid schedule — like calling every day at precisely 8:05 AM.
• Don’t rush to call back. Scammers often count on proactive victims who are curious enough to return calls from unfamiliar numbers. On top of that, calling back could end up costing you money if it’s a premium-rate number.
• Don’t speak first. Wait for the caller to greet you before starting a conversation. If you hear muffled noise or complete silence on the line, hang up and save yourself the hassle — it’s likely a scam.
• Block unknown numbers — even after the call. If you picked up and realized the call could be risky, it’s best to block the number right away. You can use the built-in features on most modern smartphones to do this.
• Don’t share your number everywhere. Phishing sites, fly-by-night web pages, and sketchy giveaways often exist solely to collect your personal data. When filling out forms online, it doesn’t hurt to use a burner or secondary number.
• Get a second phone number. Separate your daily life between two numbers. Use your main line strictly for family, friends, and work contacts, and reserve the secondary line for deliveries, online marketplaces, and general web sign-ups.

Further reading on scammers and deepfakes:

• They’re reading you like a book: scammers’ favorite tricks
• How to recognize a deepfake: attack of the clones
• The great messaging heist targeting your wallet
• You’re in for a big payout again
• Scareware: fake threats, real risks

Cybersecurity news increasingly focuses on attackers using AI for reconnaissance, vulnerability hunting, and writing scripts or malware. Against this backdrop, a less conventional category of attacks remains largely overlooked: threat actors leveraging AI tools that are already deployed within the target infrastructure.

AI agents designed for coding, as well as command-line tools — such as Claude Code CLI, Gemini CLI, Codex CLI, Amazon Q CLI, and the like — are particularly attractive targets. These agents can read and modify files, execute shell commands, install packages, and connect to external services. Developers frequently allow them to run in autonomous mode to avoid manually confirming every action. For an attacker, such an agent becomes a ready-made multitool that has the right privileges, access to critical data, and security clearances from the get-go. That said, other types of AI agents, such as automated financial workflow assistants, are equally vulnerable to exploitation.

Why trusted agents pose a threat

An AI agent differs from standard software in its broad access privileges, the wide array of ways it can be prompted to execute actions, and the difficulty of detecting its anomalous behavior. Enterprise security controls (EPP, EDR, XDR, SIEM) scan for malicious files, known command patterns, and suspicious action sequences. Even if a specific strain of malware hasn’t been seen before, its behavior often gives it away: bulk searches for documents, spawning PowerShell processes, accessing credential stores, or exfiltrating data to an external server from an unknown application will trigger a deluge of alerts in the SOC.

It’s much harder to apply that same detection logic to a legitimate, pre-installed AI agent. By design, these tools are capable of executing an extremely broad range of operations. Running scripts, reading configuration files (including secrets), moving documents, and installing dependencies can all be a part of their normal operation — especially on a developer’s machine.

When an attacker attempts to exploit this kind of AI system, they use natural language to convey their instructions to the agent. A request to hunt for passwords, API keys, or cryptocurrency wallets can be framed in thousands of ways, expressed in a variety of languages, or phrased indirectly. Furthermore, a malicious command can be hidden in any data stream the agent reads — including text fields in documents, file names, or even error logs, as shown in one of the examples below.

Integration between agents and third-party software further expands the attack surface. Agents are frequently connected to email, instant messaging apps, task management systems, knowledge bases, and MCP servers. As a result, a malicious instruction can originate from virtually any source whose data the agent processes automatically.

Below are several examples of attacks where threat actors leveraged AI agents to achieve their goals.

The Nx attack: a coding agent hunting for passwords

The most prominent and widespread example is the compromise of Nx npm packages in August 2025, dubbed s1ngularity. Attackers exploited a vulnerable GitHub Actions workflow, stole a package-publishing token, and released trojanized versions of several @nx packages that harvested developer secrets.

The malicious post-install script checked whether Claude Code, Gemini CLI, or Amazon Q CLI were installed on the victim’s machine. If any of these were detected, the agent was issued an instruction to scan for cryptocurrency wallets, ENV files, API keys, and other sensitive data. The agents were executed with auto-approval flags enabled, such as --dangerously-skip-permissions, --yolo, and --trust-all-tools.

The search results were then exfiltrated to public GitHub repositories created by the attackers. Over several waves of the attack, thousands of secrets belonging to hundreds of organizations were exposed.

Instead of developing a complex, custom harvesting tool, the attackers repurposed the victim’s own legitimate agent. It already could navigate the file system, understand file context, and identify high-value target data. However, post-incident analysis by security experts revealed drawbacks to this approach: the AI occasionally refused to carry out unethical tasks. Furthermore, the malware authors had to adjust their code so that it “waited” for the AI agent, which could take a considerable amount of time to process the task while generating significant intermediate output.

Communication via… error logs

The AgentJacking study was not a malicious attack, but it differed from one only in intent: its authors had no interest in stealing secrets. After developing the attack technique, the researchers tested it on a global scale and received callback responses from AI agents belonging to more than a hundred real-world companies — some of them major enterprises.

The attack targeted Sentry, a widely used telemetry service. Websites and mobile applications automatically transmit error reports to Sentry so developers can monitor issues without requiring active user reporting. These reports are accepted without authentication because errors often occur for anonymous visitors that the site or application knows nothing about.

The researchers configured their own MCP server within Sentry, allowing AI agents to analyze reports automatically. Next, they submitted a fake error message containing a data block formatted to match Sentry’s MCP server data structure. Embedded inside was an indirect prompt injection — an instruction to run “additional diagnostics” via a command like: npx @controlled-validation-package --diagnose.

If error handling were delegated to an agent, it could take the instruction as legitimate and execute a command resulting in the automatic installation of a third-party package. In this experiment, the package merely contacted the researchers’ command-and-control server without exfiltrating any sensitive data. A genuine malicious package, however, could steal credentials, tamper with code, or establish persistence in the system. Identifying targets for such an attack is equally straightforward: an organization’s use of Sentry is easily detectable by analyzing website code or checking the strings in mobile application binaries.

Notably, according to the study’s authors, Sentry acknowledged the issue but stopped short of implementing comprehensive fixes, opting instead to block only the specific variant of the malicious query routed through the MCP. Screenshots included in the study reveal that a Sentry employee described the system as “technically not defensible” against the attack.

Stealing financial data via MCP

Abusing unsecured MCP servers — as seen in the Sentry case — is just one way to attack this protocol. Introduced just a year ago, and already dubbed “USB for AI agents”, the Model Context Protocol standard was adopted rapidly without robust built-in security controls. As a result, attacks targeting MCP are bound to proliferate — a trend Microsoft recently warned about.

Threats can lie hidden even within the description of an MCP tool before the agent ever uses it. Agents parse tool descriptions to determine when to call the tool and what data to supply. If an MCP server provider stealthily alters a description — adding an instruction that demands collection of additional data — the agent may execute it as part of its standard workflow.

Microsoft highlights a scenario involving a financial agent that verifies vendor banking details via an external MCP service. Following a malicious update to the tool’s description, the agent begins appending unpaid-invoice data to its responses. The user sees a normal response, while confidential financial data is quietly exfiltrated to the server owner. Individually, each operation appears authorized: the agent operates under the employee’s permissions, queries an approved service, and uses a standard interface. Yet a data breach still occurs.

It remains unclear from Microsoft’s ambiguous phrasing whether this example is purely hypothetical or based on an investigation into a live incident. However, the fact that the post originated from Microsoft Incident Response points to the latter.

Prompt injections have already gone mainstream

In 2026, Unit 42 researchers identified widespread attempts to covertly embed instructions designed for AI systems across numerous public websites. Many of these instructions aim to force models to reveal system prompts, approve sites and posts during ad placement reviews, or boost phishing pages through SEO manipulation.

Similarly, an analysis of Common Crawl data by Google recorded a surge in such material: between November 2025 and February 2026, the share of malicious injections increased by 32%.

That said, confirmed cases of successful compromise using these techniques remain scarce. The presence of an injection on a webpage does not guarantee that an agent ever actually executed the command. Nevertheless, threat actors have clearly begun optimizing web content for an era when it will be read not just — and perhaps not primarily — by humans.

How to defend against agent-based attacks

The key takeaway for cybersecurity teams and all AI users alike is simple: an AI agent should never be considered trusted merely because it has been officially approved for enterprise use.

Organizations must:

• Maintain an inventory of agents, MCP servers, and the various tools available for them. Use allowlists to restrict agent tools, and package installations to components explicitly vetted and approved by information security. Allowlists must specify exact package versions.
• Monitor version changes to MCP tools and other dependencies. Re-evaluate updates before adding them to the approved inventory.
• Apply the principle of least privilege. Grant agents only the specific permissions needed for a given task, and only for the duration of that task.
• Enforce human-in-the-loop approval for package installations, script executions, file sends, and other high-risk actions.
• Run agents in isolated environments with limited access to developer workstations.
• Disable dangerous auto-approval modes.
• Restrict outbound network connections, and permit data transmission only for approved services.
• Store secrets in centralized, secure vaults, using short-lived tokens and rotating them regularly.
• Forward prompt logs, tool calls, shell commands, and agent network traffic into SIEM and XDR systems. For these tools to be effective, they require deep operational context regarding agent activity alongside specialized rule sets tailored to AI data processing pipelines.